{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "from torch.optim import Optimizer\n",
    "\n",
    "\n",
    "import collections\n",
    "import h5py, sys\n",
    "import gzip\n",
    "import os\n",
    "import math\n",
    "\n",
    "\n",
    "try:\n",
    "    import cPickle as pickle\n",
    "except:\n",
    "    import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mkdir(paths):\n",
    "    if not isinstance(paths, (list, tuple)):\n",
    "        paths = [paths]\n",
    "    for path in paths:\n",
    "        if not os.path.isdir(path):\n",
    "            os.makedirs(path)\n",
    "\n",
    "from __future__ import print_function\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "\n",
    "suffixes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']\n",
    "def humansize(nbytes):\n",
    "    i = 0\n",
    "    while nbytes >= 1024 and i < len(suffixes)-1:\n",
    "        nbytes /= 1024.\n",
    "        i += 1\n",
    "    f = ('%.2f' % nbytes)\n",
    "    return '%s%s' % (f, suffixes[i])\n",
    "\n",
    "\n",
    "def get_num_batches(nb_samples, batch_size, roundup=True):\n",
    "    if roundup:\n",
    "        return ((nb_samples + (-nb_samples % batch_size)) / batch_size)  # roundup division\n",
    "    else:\n",
    "        return nb_samples / batch_size\n",
    "\n",
    "def generate_ind_batch(nb_samples, batch_size, random=True, roundup=True):\n",
    "    if random:\n",
    "        ind = np.random.permutation(nb_samples)\n",
    "    else:\n",
    "        ind = range(int(nb_samples))\n",
    "    for i in range(int(get_num_batches(nb_samples, batch_size, roundup))):\n",
    "        yield ind[i * batch_size: (i + 1) * batch_size]\n",
    "\n",
    "def to_variable(var=(), cuda=True, volatile=False):\n",
    "    out = []\n",
    "    for v in var:\n",
    "        if isinstance(v, np.ndarray):\n",
    "            v = torch.from_numpy(v).type(torch.FloatTensor)\n",
    "\n",
    "        if not v.is_cuda and cuda:\n",
    "            v = v.cuda()\n",
    "\n",
    "        if not isinstance(v, Variable):\n",
    "            v = Variable(v, volatile=volatile)\n",
    "\n",
    "        out.append(v)\n",
    "    return out\n",
    "  \n",
    "def cprint(color, text, **kwargs):\n",
    "    if color[0] == '*':\n",
    "        pre_code = '1;'\n",
    "        color = color[1:]\n",
    "    else:\n",
    "        pre_code = ''\n",
    "    code = {\n",
    "        'a': '30',\n",
    "        'r': '31',\n",
    "        'g': '32',\n",
    "        'y': '33',\n",
    "        'b': '34',\n",
    "        'p': '35',\n",
    "        'c': '36',\n",
    "        'w': '37'\n",
    "    }\n",
    "    print(\"\\x1b[%s%sm%s\\x1b[0m\" % (pre_code, code[color], text), **kwargs)\n",
    "    sys.stdout.flush()\n",
    "\n",
    "def shuffle_in_unison_scary(a, b):\n",
    "    rng_state = np.random.get_state()\n",
    "    np.random.shuffle(a)\n",
    "    np.random.set_state(rng_state)\n",
    "    np.random.shuffle(b)\n",
    "    \n",
    "    \n",
    "import torch.utils.data as data\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloader functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Datafeed(data.Dataset):\n",
    "\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)\n",
    "\n",
    "class DatafeedImage(data.Dataset):\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        img = Image.fromarray(np.uint8(img))\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "class BaseNet(object):\n",
    "    def __init__(self):\n",
    "        cprint('c', '\\nNet:')\n",
    "\n",
    "    def get_nb_parameters(self):\n",
    "        return np.sum(p.numel() for p in self.model.parameters())\n",
    "\n",
    "    def set_mode_train(self, train=True):\n",
    "        if train:\n",
    "            self.model.train()\n",
    "        else:\n",
    "            self.model.eval()\n",
    "\n",
    "    def update_lr(self, epoch, gamma=0.99):\n",
    "        self.epoch += 1\n",
    "        if self.schedule is not None:\n",
    "            if len(self.schedule) == 0 or epoch in self.schedule:\n",
    "                self.lr *= gamma\n",
    "                print('learning rate: %f  (%d)\\n' % self.lr, epoch)\n",
    "                for param_group in self.optimizer.param_groups:\n",
    "                    param_group['lr'] = self.lr\n",
    "\n",
    "    def save(self, filename):\n",
    "        cprint('c', 'Writting %s\\n' % filename)\n",
    "        torch.save({\n",
    "            'epoch': self.epoch,\n",
    "            'lr': self.lr,\n",
    "            'model': self.model,\n",
    "            'optimizer': self.optimizer}, filename)\n",
    "\n",
    "    def load(self, filename):\n",
    "        cprint('c', 'Reading %s\\n' % filename)\n",
    "        state_dict = torch.load(filename)\n",
    "        self.epoch = state_dict['epoch']\n",
    "        self.lr = state_dict['lr']\n",
    "        self.model = state_dict['model']\n",
    "        self.optimizer = state_dict['optimizer']\n",
    "        print('  restoring epoch: %d, lr: %f' % (self.epoch, self.lr))\n",
    "        return self.epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Our special classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Priors classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class laplace_prior(object):\n",
    "    def __init__(self, mu, b):\n",
    "        self.mu = mu\n",
    "        self.b = b\n",
    "\n",
    "    def loglike(self, x, do_sum=True):\n",
    "        if do_sum:\n",
    "            return (-np.log(2*self.b) - torch.abs(x - self.mu)/self.b).sum()\n",
    "        else:\n",
    "            return (-np.log(2*self.b) - torch.abs(x - self.mu)/self.b)\n",
    "\n",
    "class isotropic_gauss_prior(object):\n",
    "    def __init__(self, mu, sigma):\n",
    "        self.mu = mu\n",
    "        self.sigma = sigma\n",
    "        \n",
    "        self.cte_term = -(0.5)*np.log(2*np.pi)\n",
    "        self.det_sig_term = -np.log(self.sigma)\n",
    "\n",
    "    def loglike(self, x, do_sum=True):\n",
    "        \n",
    "        dist_term = -(0.5)*((x - self.mu)/self.sigma)**2\n",
    "        if do_sum:\n",
    "            return (self.cte_term + self.det_sig_term + dist_term).sum()\n",
    "        else:\n",
    "            return (self.cte_term + self.det_sig_term + dist_term)\n",
    "    \n",
    "class spike_slab_2GMM(object):\n",
    "    def __init__(self, mu1, mu2, sigma1, sigma2, pi, do_sum=True):\n",
    "        \n",
    "        self.N1 = isotropic_gauss_prior(mu1, sigma1)\n",
    "        self.N2 = isotropic_gauss_prior(mu2, sigma2)\n",
    "        self.do_sum = do_sum\n",
    "        \n",
    "        self.pi1 = pi \n",
    "        self.pi2 = (1-pi)\n",
    "\n",
    "    def loglike(self, x):\n",
    "        \n",
    "        N1_ll = self.N1.loglike(x, self.do_sum)\n",
    "        N2_ll = self.N2.loglike(x, self.do_sum)\n",
    "\n",
    "        # Numerical stability trick -> unnormalising logprobs will underflow otherwise\n",
    "        max_loglike = torch.max(N1_ll, N2_ll)\n",
    "            \n",
    "        normalised_like = self.pi1 + torch.exp(N1_ll - max_loglike) + self.pi2 + torch.exp(N2_ll - max_loglike)\n",
    "        loglike = torch.log(normalised_like) + max_loglike\n",
    "    \n",
    "        return loglike\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isnan(tensor):\n",
    "  # Gross: https://github.com/pytorch/pytorch/issues/4767\n",
    "    return (tensor != tensor)\n",
    "\n",
    "def hasnan(tensor):\n",
    "    return isnan(tensor).any()\n",
    "\n",
    "def isotropic_gauss_loglike(x, mu, sigma, do_sum=True):\n",
    "    cte_term = -(0.5)*np.log(2*np.pi)\n",
    "    det_sig_term = -torch.log(sigma)\n",
    "    inner = (x - mu)/sigma\n",
    "    dist_term = -(0.5)*(inner**2)\n",
    "    \n",
    "    if do_sum:\n",
    "        out = (cte_term + det_sig_term + dist_term).sum() # sum over all weights\n",
    "    else:\n",
    "        out = (cte_term + det_sig_term + dist_term)\n",
    "#     print(hasnan(x.data), hasnan(mu.data), hasnan(sigma.data), out)\n",
    "#     if isnan(out) or hasnan(out):\n",
    "#         print('NAaaanNNN')\n",
    "#         print('mu max', mu.max())\n",
    "#         print('mu min', mu.min())\n",
    "#         print('sigma max', sigma.max())\n",
    "#         print('sigma min', sigma.min())\n",
    "#         print('x max', x.max())\n",
    "#         print('x min', x.min())\n",
    "#         print((x - mu).max())\n",
    "#         print((x - mu).min())\n",
    "#         print(hasnan(inner))\n",
    "#         print(dist_term)\n",
    "#         print(hasnan(dist_term))\n",
    "    return out \n",
    "\n",
    "            \n",
    "            \n",
    "\n",
    "def KLD_cost(mu_p, sig_p, mu_q, sig_q):\n",
    "    \n",
    "        KLD = 0.5 * (2*torch.log(sig_p/sig_q) - 1 + (sig_q/sig_p).pow(2) + ((mu_p - mu_q)/sig_p).pow(2)).sum()\n",
    "                                 # https://arxiv.org/abs/1312.6114 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n",
    "        return KLD\n",
    "    \n",
    "    \n",
    "class BayesLinear_local_reparam(nn.Module):\n",
    "    def __init__(self, n_in, n_out):\n",
    "        super(BayesLinear_local_reparam, self).__init__()\n",
    "        self.n_in = n_in\n",
    "        self.n_out = n_out\n",
    "        \n",
    "   \n",
    "        # Learnable parameters\n",
    "        self.W_mu = nn.Parameter(torch.Tensor(self.n_in, self.n_out).uniform_(-0.2, 0.2))\n",
    "        self.W_p = nn.Parameter(torch.Tensor(self.n_in, self.n_out).uniform_(-3, -2))#.fill_(np.log(-1+np.exp(np.sqrt(2/self.n_in)))))\n",
    "        \n",
    "        self.b_mu = nn.Parameter(torch.Tensor(self.n_out).uniform_(-0.2, 0.2))\n",
    "        self.b_p = nn.Parameter( torch.Tensor(self.n_out).uniform_(-3, -2))#.fill_(np.log(-1+np.exp(np.sqrt(2/self.n_in)))))#.uniform_(-3, -2))  \n",
    "    \n",
    "                                   \n",
    "    def forward(self, X, sample=False):\n",
    "#         print(self.training)\n",
    "\n",
    "        if not self.training and not sample: # This is just a placeholder function\n",
    "            output = torch.mm(X, self.W_mu) + self.b_mu.expand(X.size()[0], self.n_out)\n",
    "            return output, 0, 0\n",
    "                                       \n",
    "        else:\n",
    "                                       \n",
    "            # calculate std         \n",
    "            std_w = 1e-6 + F.softplus(self.W_p, beta=1, threshold=20)\n",
    "            std_b = 1e-6 + F.softplus(self.b_p, beta=1, threshold=20) \n",
    "            \n",
    "                                            \n",
    "            act_W_mu = torch.mm(X, self.W_mu) #self.W_mu + std_w * eps_W\n",
    "            act_W_std = torch.sqrt(torch.mm(X.pow(2), std_w.pow(2)))\n",
    "            \n",
    "            \n",
    "            # Tensor.new()  Constructs a new tensor of the same data type as self tensor. \n",
    "            # the same random sample is used for every element in the minibatch output\n",
    "            eps_W = Variable(self.W_mu.data.new(act_W_std.size()).normal_(mean=0, std=1))\n",
    "            eps_b = Variable(self.b_mu.data.new(std_b.size()).normal_(mean=0, std=1))\n",
    "            \n",
    "            \n",
    "            act_W_out =  act_W_mu + act_W_std * eps_W# (batch_size, n_output)\n",
    "            act_b_out = self.b_mu + std_b * eps_b \n",
    "            \n",
    "            output = act_W_out + act_b_out.unsqueeze(0).expand(X.shape[0], -1)\n",
    "            \n",
    "            \n",
    "            kld = KLD_cost(mu_p=0, sig_p=0.1, mu_q=self.W_mu, sig_q=std_w) + KLD_cost(mu_p=0, sig_p=0.1, mu_q=self.b_mu, sig_q=std_b)\n",
    "            return output, kld, 0\n",
    "#         np.sqrt(2/self.n_in)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class bayes_linear_2L(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(bayes_linear_2L, self).__init__()\n",
    "        \n",
    "        n_hid = 1200\n",
    "        self.prior_instance = isotropic_gauss_prior(mu=0, sigma=0.1)\n",
    "        \n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        \n",
    "        self.bfc1 = BayesLinear_local_reparam(input_dim, n_hid)\n",
    "        self.bfc2 = BayesLinear_local_reparam(n_hid, n_hid)\n",
    "        self.bfc3 = BayesLinear_local_reparam(n_hid, output_dim)\n",
    "        \n",
    "        # choose your non linearity\n",
    "        #self.act = nn.Tanh()\n",
    "        #self.act = nn.Sigmoid()\n",
    "        self.act = nn.ReLU(inplace=True)\n",
    "        #self.act = nn.ELU(inplace=True)\n",
    "        #self.act = nn.SELU(inplace=True)\n",
    "\n",
    "    def forward(self, x, sample=False):\n",
    "        \n",
    "        tlqw = 0\n",
    "        tlpw = 0\n",
    "        \n",
    "        x = x.view(-1, self.input_dim) # view(batch_size, input_dim)\n",
    "        # -----------------\n",
    "        x, lqw, lpw = self.bfc1(x, sample)\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        x, lqw, lpw = self.bfc2(x, sample)\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        y, lqw, lpw = self.bfc3(x, sample)\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        \n",
    "        return y, tlqw, tlpw\n",
    "    \n",
    "    def sample_predict(self, x, Nsamples):\n",
    "        \n",
    "        # Just copies type from x, initializes new vector\n",
    "        predictions = x.data.new(Nsamples, x.shape[0], self.output_dim)\n",
    "        tlqw_vec = np.zeros(Nsamples)\n",
    "        tlpw_vec = np.zeros(Nsamples)\n",
    "        \n",
    "        for i in range(Nsamples):\n",
    "            \n",
    "            y, tlqw, tlpw = self.forward(x, sample=True)\n",
    "            predictions[i] = y\n",
    "            tlqw_vec[i] = tlqw\n",
    "            tlpw_vec[i] = tlpw \n",
    "            \n",
    "        return predictions, tlqw_vec, tlpw_vec\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_weights(W_mu, b_mu, W_p, b_p):\n",
    "    \n",
    "    eps_W = W_mu.data.new(W_mu.size()).normal_()\n",
    "    # sample parameters         \n",
    "    std_w = 1e-6 + F.softplus(W_p, beta=1, threshold=20)     \n",
    "    W = W_mu + 1 * std_w * eps_W\n",
    "    \n",
    "    if b_mu is not None:\n",
    "        std_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)  \n",
    "        eps_b = b_mu.data.new(b_mu.size()).normal_()\n",
    "        b = b_mu + 1 * std_b * eps_b\n",
    "    else:\n",
    "        b = None\n",
    "    \n",
    "    return W, b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "\n",
    "class Bayes_Net(BaseNet):\n",
    "    eps = 1e-6\n",
    "\n",
    "    def __init__(self, lr=1e-3, channels_in=3, side_in=28, cuda=True, classes=10, batch_size=128, Nbatches=0):\n",
    "        super(Bayes_Net, self).__init__()\n",
    "        cprint('y', ' Creating Net!! ')\n",
    "        self.lr = lr\n",
    "        self.schedule = None  # [] #[50,200,400,600]\n",
    "        self.cuda = cuda\n",
    "        self.channels_in = channels_in\n",
    "        self.classes = classes\n",
    "        self.batch_size = batch_size\n",
    "        self.Nbatches = Nbatches\n",
    "        self.side_in=side_in\n",
    "        self.create_net()\n",
    "        self.create_opt()\n",
    "        self.epoch = 0\n",
    "\n",
    "        self.test=False\n",
    "\n",
    "    def create_net(self):\n",
    "        torch.manual_seed(42)\n",
    "        if self.cuda:\n",
    "            torch.cuda.manual_seed(42)\n",
    "\n",
    "        self.model = bayes_linear_2L(input_dim=self.channels_in*self.side_in*self.side_in, output_dim=self.classes)\n",
    "#         print(self.model.prior_instance)\n",
    "        if self.cuda:\n",
    "            self.model.cuda()\n",
    "#             cudnn.benchmark = True\n",
    "\n",
    "        print('    Total params: %.2fM' % (self.get_nb_parameters() / 1000000.0))\n",
    "\n",
    "    def create_opt(self):\n",
    "#         self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr, betas=(0.9, 0.999), eps=1e-08,\n",
    "#                                           weight_decay=0)\n",
    "        self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0)\n",
    "\n",
    "    #         self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0.9)\n",
    "#         self.sched = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=1, gamma=10, last_epoch=-1)\n",
    "\n",
    "    def fit(self, x, y, samples=1):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        if samples == 1:\n",
    "            out, tlqw, tlpw = self.model(x)\n",
    "            mlpdw = F.cross_entropy(out, y, reduction='sum')\n",
    "            Edkl = (tlqw - tlpw)/self.Nbatches\n",
    "            \n",
    "        elif samples > 1:\n",
    "            mlpdw_cum = 0\n",
    "            Edkl_cum = 0\n",
    "            \n",
    "            for i in range(samples):\n",
    "                out, tlqw, tlpw = self.model(x, sample=True)\n",
    "                mlpdw_i = F.cross_entropy(out, y, reduction='sum')\n",
    "                Edkl_i = (tlqw - tlpw)/self.Nbatches\n",
    "                mlpdw_cum = mlpdw_cum + mlpdw_i\n",
    "                Edkl_cum = Edkl_cum + Edkl_i \n",
    "            \n",
    "            mlpdw = mlpdw_cum/samples\n",
    "            Edkl = Edkl_cum/samples\n",
    "        \n",
    "        loss = Edkl + mlpdw\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        # out: (batch_size, out_channels, out_caps_dims)\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return Edkl.data, mlpdw.data, err\n",
    "\n",
    "    def eval(self, x, y, train=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out, _, _ = self.model(x)\n",
    "\n",
    "        loss = F.cross_entropy(out, y, reduction='sum')\n",
    "\n",
    "        probs = F.softmax(out, dim=1).data.cpu()\n",
    "\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    def sample_eval(self, x, y, Nsamples, logits=True, train=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out, _, _ = self.model.sample_predict(x, Nsamples)\n",
    "        \n",
    "        if logits:\n",
    "            mean_out = out.mean(dim=0, keepdim=False)\n",
    "            loss = F.cross_entropy(mean_out, y, reduction='sum')\n",
    "            probs = F.softmax(mean_out, dim=1).data.cpu()\n",
    "            \n",
    "        else:\n",
    "            mean_out =  F.softmax(out, dim=2).mean(dim=0, keepdim=False)\n",
    "            probs = mean_out.data.cpu()\n",
    "            \n",
    "            log_mean_probs_out = torch.log(mean_out)\n",
    "            loss = F.nll_loss(log_mean_probs_out, y, reduction='sum')\n",
    "\n",
    "        pred = mean_out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    def all_sample_eval(self, x, y, Nsamples):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out, _, _ = self.model.sample_predict(x, Nsamples)\n",
    "        \n",
    "        prob_out =  F.softmax(out, dim=2)\n",
    "        prob_out = prob_out.data\n",
    "\n",
    "        return prob_out\n",
    "    \n",
    "    def get_weight_samples(self, Nsamples=10):\n",
    "        state_dict = self.model.state_dict()\n",
    "        weight_vec = []\n",
    "        \n",
    "        for i in range(Nsamples):\n",
    "            previous_layer_name = ''\n",
    "            for key in state_dict.keys():\n",
    "                layer_name = key.split('.')[0]\n",
    "                if layer_name != previous_layer_name:\n",
    "                    previous_layer_name = layer_name\n",
    "\n",
    "                    W_mu = state_dict[layer_name+'.W_mu'].data\n",
    "                    W_p = state_dict[layer_name+'.W_p'].data\n",
    "\n",
    "    #                 b_mu = state_dict[layer_name+'.b_mu'].cpu().data\n",
    "    #                 b_p = state_dict[layer_name+'.b_p'].cpu().data\n",
    "\n",
    "                    W, b = sample_weights(W_mu=W_mu, b_mu=None, W_p=W_p, b_p=None)\n",
    "\n",
    "                    for weight in W.cpu().view(-1):\n",
    "                        weight_vec.append(weight)\n",
    "            \n",
    "        return np.array(weight_vec)\n",
    "    \n",
    "    def get_weight_SNR(self, thresh=None):\n",
    "        state_dict = self.model.state_dict()\n",
    "        weight_SNR_vec = []\n",
    "        \n",
    "        if thresh is not None:\n",
    "            mask_dict = {}\n",
    "        \n",
    "        previous_layer_name = ''\n",
    "        for key in state_dict.keys():\n",
    "            layer_name = key.split('.')[0]\n",
    "            if layer_name != previous_layer_name:\n",
    "                previous_layer_name = layer_name\n",
    "\n",
    "                W_mu = state_dict[layer_name+'.W_mu'].data\n",
    "                W_p = state_dict[layer_name+'.W_p'].data\n",
    "                sig_W = 1e-6 + F.softplus(W_p, beta=1, threshold=20)\n",
    "\n",
    "                b_mu = state_dict[layer_name+'.b_mu'].data\n",
    "                b_p = state_dict[layer_name+'.b_p'].data\n",
    "                sig_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)\n",
    "\n",
    "                W_snr = (torch.abs(W_mu)/sig_W)\n",
    "                b_snr = (torch.abs(b_mu)/sig_b)\n",
    "                \n",
    "                if thresh is not None:\n",
    "                    mask_dict[layer_name+'.W'] = W_snr > thresh\n",
    "                    mask_dict[layer_name+'.b'] = b_snr > thresh\n",
    "                    \n",
    "                else:\n",
    "                \n",
    "                    for weight_SNR in W_snr.cpu().view(-1):\n",
    "                        weight_SNR_vec.append(weight_SNR)\n",
    "\n",
    "                    for weight_SNR in b_snr.cpu().view(-1):\n",
    "                        weight_SNR_vec.append(weight_SNR)\n",
    "        \n",
    "        if thresh is not None:\n",
    "            return mask_dict\n",
    "        else:\n",
    "            return np.array(weight_SNR_vec)\n",
    "    \n",
    "    \n",
    "    def get_weight_KLD(self, Nsamples=20, thresh=None):\n",
    "        state_dict = self.model.state_dict()\n",
    "        weight_KLD_vec = []\n",
    "        \n",
    "        if thresh is not None:\n",
    "            mask_dict = {}\n",
    "        \n",
    "        previous_layer_name = ''\n",
    "        for key in state_dict.keys():\n",
    "            layer_name = key.split('.')[0]\n",
    "            if layer_name != previous_layer_name:\n",
    "                previous_layer_name = layer_name\n",
    "\n",
    "                W_mu = state_dict[layer_name+'.W_mu'].data\n",
    "                W_p = state_dict[layer_name+'.W_p'].data\n",
    "                b_mu = state_dict[layer_name+'.b_mu'].data\n",
    "                b_p = state_dict[layer_name+'.b_p'].data\n",
    "                \n",
    "                std_w = 1e-6 + F.softplus(W_p, beta=1, threshold=20)  \n",
    "                std_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)\n",
    "\n",
    "                KL_W = W_mu.new(W_mu.size()).zero_()\n",
    "                KL_b = b_mu.new(b_mu.size()).zero_()\n",
    "                for i in range(Nsamples):\n",
    "                    W, b = sample_weights(W_mu=W_mu, b_mu=b_mu, W_p=W_p, b_p=b_p)  \n",
    "                    # Note that this will currently not work with slab and spike prior\n",
    "                    KL_W += isotropic_gauss_loglike(W, W_mu, std_w, do_sum=False) - self.model.prior_instance.loglike(W, do_sum=False)\n",
    "                    KL_b += isotropic_gauss_loglike(b, b_mu, std_b, do_sum=False) - self.model.prior_instance.loglike(b, do_sum=False)\n",
    "\n",
    "                KL_W /= Nsamples\n",
    "                KL_b /= Nsamples\n",
    "                \n",
    "                if thresh is not None:\n",
    "                    mask_dict[layer_name+'.W'] = KL_W > thresh\n",
    "                    mask_dict[layer_name+'.b'] = KL_b > thresh\n",
    "                    \n",
    "                else:\n",
    "\n",
    "                    for weight_KLD in KL_W.cpu().view(-1):\n",
    "                        weight_KLD_vec.append(weight_KLD)\n",
    "\n",
    "                    for weight_KLD in KL_b.cpu().view(-1):\n",
    "                        weight_KLD_vec.append(weight_KLD)\n",
    "        \n",
    "        if thresh is not None:\n",
    "            return mask_dict\n",
    "        else:    \n",
    "            return np.array(weight_KLD_vec)\n",
    "        \n",
    "        \n",
    "    def mask_model(self, Nsamples=0, thresh=0):\n",
    "        '''\n",
    "        Nsamples is used to select SNR (0) or KLD (>0) based masking\n",
    "        '''\n",
    "        original_state_dict = copy.deepcopy(self.model.state_dict())\n",
    "        state_dict = self.model.state_dict()\n",
    "        \n",
    "        if Nsamples == 0:\n",
    "            mask_dict = self.get_weight_SNR(thresh=thresh)\n",
    "        else:\n",
    "            mask_dict = self.get_weight_KLD(Nsamples=Nsamples, thresh=thresh)\n",
    "        \n",
    "        n_unmasked = 0\n",
    "        \n",
    "        previous_layer_name = ''\n",
    "        for key in state_dict.keys():\n",
    "            layer_name = key.split('.')[0]\n",
    "            if layer_name != previous_layer_name:\n",
    "                previous_layer_name = layer_name\n",
    "                \n",
    "                state_dict[layer_name+'.W_mu'][1-mask_dict[layer_name+'.W']] = 0\n",
    "                state_dict[layer_name+'.W_p'][1-mask_dict[layer_name+'.W']] = -1000\n",
    "                \n",
    "                state_dict[layer_name+'.b_mu'][1-mask_dict[layer_name+'.b']] = 0\n",
    "                state_dict[layer_name+'.b_p'][1-mask_dict[layer_name+'.b']] = -1000\n",
    "                \n",
    "                \n",
    "                n_unmasked += mask_dict[layer_name+'.W'].sum()\n",
    "                n_unmasked += mask_dict[layer_name+'.b'].sum()\n",
    "                \n",
    "        return original_state_dict, n_unmasked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 4.79M\n",
      "\u001b[36m\n",
      "Train:\u001b[0m\n",
      "  init cost variables:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-0ecdb788b35d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[0mcost_dkl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcost_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnsamples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0merr_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m         \u001b[0mkl_cost_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mcost_dkl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m         \u001b[0mpred_cost_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mcost_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "import copy\n",
    "\n",
    "\n",
    "models_dir = 'models_weight_uncertainty_local_reparam_MNIST'\n",
    "results_dir = 'results_weight_uncertainty_local_reparam_MNIST'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 100\n",
    "nb_epochs = 140\n",
    "log_interval = 1\n",
    "\n",
    "savemodel_its = [20, 50, 80, 120]\n",
    "save_dicts = []\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-3\n",
    "nsamples = 1\n",
    "########################################################################################\n",
    "net = Bayes_Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size,\n",
    "          Nbatches=(NTrainPointsMNIST/batch_size))\n",
    "\n",
    "epoch = 0\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# train\n",
    "cprint('c', '\\nTrain:')\n",
    "\n",
    "print('  init cost variables:')\n",
    "kl_cost_train = np.zeros(nb_epochs)\n",
    "pred_cost_train = np.zeros(nb_epochs)\n",
    "err_train = np.zeros(nb_epochs)\n",
    "\n",
    "cost_dev = np.zeros(nb_epochs)\n",
    "err_dev = np.zeros(nb_epochs)\n",
    "# best_cost = np.inf\n",
    "best_err = np.inf\n",
    "\n",
    "\n",
    "nb_its_dev = 1\n",
    "\n",
    "tic0 = time.time()\n",
    "for i in range(epoch, nb_epochs):\n",
    "    \n",
    "#     if i in [1]:\n",
    "#         print('updating lr')\n",
    "#         net.sched.step()\n",
    "    \n",
    "    net.set_mode_train(True)\n",
    "\n",
    "    tic = time.time()\n",
    "    nb_samples = 0\n",
    "\n",
    "    for x, y in trainloader:\n",
    "        cost_dkl, cost_pred, err = net.fit(x, y, samples=nsamples)\n",
    "\n",
    "        err_train[i] += err\n",
    "        kl_cost_train[i] += cost_dkl\n",
    "        pred_cost_train[i] += cost_pred\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    kl_cost_train[i] /= nb_samples\n",
    "    pred_cost_train[i] /= nb_samples\n",
    "    err_train[i] /= nb_samples\n",
    "\n",
    "    toc = time.time()\n",
    "    net.epoch = i\n",
    "    # ---- print\n",
    "    print(\"it %d/%d, Jtr_KL = %f, Jtr_pred = %f, err = %f, \" % (i, nb_epochs, kl_cost_train[i], pred_cost_train[i], err_train[i]), end=\"\")\n",
    "    cprint('r', '   time: %f seconds\\n' % (toc - tic))\n",
    "\n",
    "    # Save state dict\n",
    "    if i in savemodel_its:\n",
    "        save_dicts.append(copy.deepcopy(net.model.state_dict()))\n",
    "    \n",
    "    # Reduce variance\n",
    "    if i == 60:\n",
    "        nsamples = 3\n",
    "    \n",
    "    # ---- dev\n",
    "    if i % nb_its_dev == 0:\n",
    "        net.set_mode_train(False)\n",
    "        nb_samples = 0\n",
    "        for j, (x, y) in enumerate(valloader):\n",
    "\n",
    "            cost, err, probs = net.eval(x, y)\n",
    "\n",
    "            cost_dev[i] += cost\n",
    "            err_dev[i] += err\n",
    "            nb_samples += len(x)\n",
    "\n",
    "        cost_dev[i] /= nb_samples\n",
    "        err_dev[i] /= nb_samples\n",
    "\n",
    "        cprint('g', '    Jdev = %f, err = %f\\n' % (cost_dev[i], err_dev[i]))\n",
    "\n",
    "        if err_dev[i] < best_err:\n",
    "            best_err = err_dev[i]\n",
    "            cprint('b', 'best test error')\n",
    "            net.save(models_dir+'/theta_best.dat')\n",
    "\n",
    "toc0 = time.time()\n",
    "runtime_per_it = (toc0 - tic0) / float(nb_epochs)\n",
    "cprint('r', '   average time: %f seconds\\n' % runtime_per_it)\n",
    "\n",
    "net.save(models_dir+'/theta_last.dat')\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# results\n",
    "cprint('c', '\\nRESULTS:')\n",
    "nb_parameters = net.get_nb_parameters()\n",
    "best_cost_dev = np.min(cost_dev)\n",
    "best_cost_train = np.min(pred_cost_train)\n",
    "err_dev_min = err_dev[::nb_its_dev].min()\n",
    "\n",
    "print('  cost_dev: %f (cost_train %f)' % (best_cost_dev, best_cost_train))\n",
    "print('  err_dev: %f' % (err_dev_min))\n",
    "print('  nb_parameters: %d (%s)' % (nb_parameters, humansize(nb_parameters)))\n",
    "print('  time_per_it: %fs\\n' % (runtime_per_it))\n",
    "\n",
    "\n",
    "\n",
    "## Save results for plots\n",
    "# np.save('results/test_predictions.npy', test_predictions)\n",
    "np.save(results_dir + '/cost_train.npy', kl_cost_train)\n",
    "np.save(results_dir + '/cost_train.npy', pred_cost_train)\n",
    "np.save(results_dir + '/cost_dev.npy', cost_dev)\n",
    "np.save(results_dir + '/err_train.npy', err_train)\n",
    "np.save(results_dir + '/err_dev.npy', err_dev)\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# fig cost vs its\n",
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "plt.figure()\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(kl_cost_train, 'r')\n",
    "ax1.set_ylabel('nats?')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax = plt.gca()\n",
    "plt.title('DKL (per sample)')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), box_inches='tight')\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "plt.figure()\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(kl_cost_train, 'r')\n",
    "ax1.set_ylabel('nats?')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax = plt.gca()\n",
    "plt.title('DKL (per sample)')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load up saved model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "<__main__.isotropic_gauss_prior object at 0x7fbba87a3710>\n",
      "    Total params: 4.79M\n",
      "\u001b[36mReading models_weight_uncertainty_local_reparam_MNIST/theta_last.dat\n",
      "\u001b[0m\n",
      "  restoring epoch: 139, lr: 0.001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "139"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "import copy\n",
    "\n",
    "\n",
    "models_dir = 'models_weight_uncertainty_local_reparam_MNIST'\n",
    "results_dir = 'results_weight_uncertainty_local_reparam_MNIST'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 100\n",
    "nb_epochs = 140\n",
    "log_interval = 1\n",
    "\n",
    "savemodel_its = [20, 50, 80, 120]\n",
    "save_dicts = []\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-3\n",
    "nsamples = 1\n",
    "########################################################################################\n",
    "net = Bayes_Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size,\n",
    "          Nbatches=(NTrainPointsMNIST/batch_size))\n",
    "\n",
    "\n",
    "net.load(models_dir+'/theta_last.dat') # theta_last.dat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bayes_linear_2L(\n",
      "  (bfc1): BayesLinear_local_reparam()\n",
      "  (bfc2): BayesLinear_local_reparam()\n",
      "  (bfc3): BayesLinear_local_reparam()\n",
      "  (act): ReLU(inplace)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(net.model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inference with sampling on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m    Loglike = -1086.433716, err = 0.026100\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "batch_size = 200\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "nb_samples = 0\n",
    "test_predictions = np.zeros((10000, 10))\n",
    "\n",
    "Nsamples = 100\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "for j, (x, y) in enumerate(valloader):\n",
    "    cost, err, probs = net.sample_eval(x, y, Nsamples, logits=False) # , logits=True\n",
    "\n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "    test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "# test_cost /= nb_samples\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Loglike = %5.6f, err = %1.6f\\n' % (-test_cost, test_err))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rotations, Bayesian [IGNORE THIS FOR NOW]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First load data into numpy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 1, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "x_dev = []\n",
    "y_dev = []\n",
    "for x, y in valloader:\n",
    "    x_dev.append(x.cpu().numpy())\n",
    "    y_dev.append(y.cpu().numpy())\n",
    "\n",
    "x_dev = np.concatenate(x_dev)\n",
    "y_dev = np.concatenate(y_dev)\n",
    "print(x_dev.shape)\n",
    "print(y_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 1, 28, 28)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:83: MatplotlibDeprecationWarning: The set_color_cycle function was deprecated in version 1.5. Use `.set_prop_cycle` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "#############\n",
    "im_ind = 90\n",
    "Nsamples = 100\n",
    "#############\n",
    "\n",
    "angle = 0\n",
    "plt.figure()\n",
    "plt.imshow( ndim.interpolation.rotate(x_dev[im_ind,0,:,:], 0, reshape=False))\n",
    "plt.title('original image')\n",
    "# plt.savefig('original_digit.png')\n",
    "\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 10\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "ims = []\n",
    "predictions = []\n",
    "# percentile_dist_confidence = []\n",
    "x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "\n",
    "fig = plt.figure(figsize=(steps, 8), dpi=80)\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "\n",
    "for i in range(len(rotations)):\n",
    "    \n",
    "    angle = rotations[i]\n",
    "    x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "    \n",
    "    \n",
    "    ax = fig.add_subplot(3, (steps-1), 2*(steps-1)+i)\n",
    "    ax.imshow(x_rot[0,:,:])\n",
    "    ax.axis('off')\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ims.append(x_rot[:,:,:])\n",
    "    \n",
    "ims = np.concatenate(ims)\n",
    "net.set_mode_train(False)\n",
    "y = np.ones(ims.shape[0])*y\n",
    "ims = np.expand_dims(ims, axis=1)\n",
    "cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False) # , logits=True\n",
    "\n",
    "predictions = probs.numpy()\n",
    "    \n",
    "    \n",
    "    \n",
    "textsize = 20\n",
    "lw = 5\n",
    "    \n",
    "print(ims.shape)\n",
    "ims = ims[:,0,:,:]\n",
    "# predictions = np.concatenate(predictions)\n",
    "#print(percentile_dist_confidence)\n",
    "\n",
    "c = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "     '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']  \n",
    "                                         \n",
    "\n",
    "# c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',\n",
    "#      '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
    "\n",
    "ax0 = plt.subplot2grid((3, steps-1), (0, 0), rowspan=2, colspan=steps-1)\n",
    "#ax0 = fig.add_subplot(2, 1, 1)\n",
    "plt.gca().set_color_cycle(c)\n",
    "ax0.plot(rotations, predictions, linewidth=lw)\n",
    "\n",
    "\n",
    "##########################\n",
    "# Dots at max\n",
    "\n",
    "for i in range(predictions.shape[1]):\n",
    "  \n",
    "    selections = (predictions[:,i] == predictions.max(axis=1))\n",
    "    for n in range(len(selections)):\n",
    "        if selections[n]:\n",
    "            ax0.plot(rotations[n], predictions[n, i], 'o', c=c[i], markersize=15.0)\n",
    "##########################  \n",
    "\n",
    "lgd = ax0.legend(['prob 0', 'prob 1', 'prob 2',\n",
    "            'prob 3', 'prob 4', 'prob 5',\n",
    "            'prob 6', 'prob 7', 'prob 8',\n",
    "            'prob 9'], loc='upper right', prop={'size': textsize, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "plt.xlabel('rotation angle')\n",
    "# plt.ylabel('probability')\n",
    "plt.title('True class: %d, Nsamples %d' % (y[0], Nsamples))\n",
    "# ax0.axis('tight')\n",
    "plt.tight_layout()\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "for item in ([ax0.title, ax0.xaxis.label, ax0.yaxis.label] +\n",
    "             ax0.get_xticklabels() + ax0.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "# plt.savefig('percentile_label_probabilities.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "# files.download('percentile_label_probabilities.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All dataset with entropy\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
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    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "    \n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "Nsamples = 100\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 16\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "\n",
    "all_preds = np.zeros((len(x_dev), steps, 10))\n",
    "all_sample_preds = np.zeros((len(x_dev), Nsamples, steps, 10))\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "for im_ind in range(len(x_dev)):\n",
    "    x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "    print(im_ind)\n",
    "    \n",
    "    ims = []\n",
    "    predictions = []\n",
    "    for i in range(len(rotations)):\n",
    "\n",
    "        angle = rotations[i]\n",
    "        x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "        ims.append(x_rot[:,:,:])\n",
    "    \n",
    "    ims = np.concatenate(ims)\n",
    "    net.set_mode_train(False)\n",
    "    y = np.ones(ims.shape[0])*y\n",
    "    ims = np.expand_dims(ims, axis=1)\n",
    "    \n",
    "#     cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False)\n",
    "    sample_probs = net.all_sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples)\n",
    "    probs = sample_probs.mean(dim=0)\n",
    "    \n",
    "    all_sample_preds[im_ind, :, :, :] = sample_probs.cpu().numpy()\n",
    "    predictions = probs.cpu().numpy()\n",
    "    all_preds[im_ind, :, :] = predictions\n",
    "   \n",
    "    \n",
    "all_preds_entropy = -(all_preds * np.log(all_preds)).sum(axis=2)\n",
    "mean_angle_entropy = all_preds_entropy.mean(axis=0)\n",
    "std_angle_entropy = all_preds_entropy.std(axis=0)\n",
    "\n",
    "correct_preds = np.zeros((len(x_dev), steps))\n",
    "for i in range(len(x_dev)):\n",
    "    correct_preds[i,:] = all_preds[i,:,y_dev[i]]\n",
    "    \n",
    "correct_mean = correct_preds.mean(axis=0)\n",
    "correct_std = correct_preds.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(results_dir+'/correct_preds.npy', correct_preds)\n",
    "np.save(results_dir+'/all_preds.npy', all_preds)\n",
    "np.save(results_dir+'/all_sample_preds.npy', all_sample_preds) #all_sample_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None):\n",
    "    ax = ax if ax is not None else plt.gca()\n",
    "    if color is None:\n",
    "        color = ax._get_lines.color_cycle.next()\n",
    "    if np.isscalar(yerr) or len(yerr) == len(y):\n",
    "        ymin = y - yerr\n",
    "        ymax = y + yerr\n",
    "    elif len(yerr) == 2:\n",
    "        ymin, ymax = yerr\n",
    "    ax.plot(x, y, color=color)\n",
    "    ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "\n",
    "errorfill(rotations, correct_mean, yerr=correct_std, color=c[2])\n",
    "          \n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel('rotation angle')\n",
    "lgd = plt.legend(['correct class', 'posterior predictive entropy'], loc='upper right',\n",
    "                 prop={'size': 15, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "ax = plt.gca()\n",
    "ax2 = ax.twinx()\n",
    "errorfill(rotations, mean_angle_entropy, yerr=std_angle_entropy, color=c[3], ax=ax2)\n",
    "\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + [ax2.title, ax2.xaxis.label, ax2.yaxis.label] +\n",
    "            ax.get_xticklabels() + ax.get_yticklabels() + ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    item.set_fontsize(15)\n",
    "    item.set_weight('normal')\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weight distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23928000,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total parameters: 2392800, samples: 10')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "Nsamples = 10\n",
    "weight_vector = net.get_weight_samples(Nsamples=Nsamples)\n",
    "np.save(results_dir+'/weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(weight_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "symlim = 0.7\n",
    "\n",
    "lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
    "\n",
    "sns.distplot(weight_vector, 500, norm_hist=False, label=name, ax=ax)\n",
    "ax.set_xlim((-symlim, symlim))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('Total parameters: %d, samples: %d' % (len(weight_vector)/Nsamples, Nsamples))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evolution over iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "last_state_dict = copy.deepcopy(net.model.state_dict())\n",
    "\n",
    "Nsamples = 10\n",
    "\n",
    "for idx, iteration in enumerate(savemodel_its):\n",
    "    \n",
    "    net.model.load_state_dict(save_dicts[idx])\n",
    "    weight_vector = net.get_weight_samples(Nsamples=Nsamples)\n",
    "    fig = plt.figure(dpi=120)\n",
    "    ax = fig.add_subplot(111)\n",
    "    symlim = 1\n",
    "    lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
    "    sns.distplot(weight_vector, 350, norm_hist=False, label='it %d' % (iteration), ax=ax)\n",
    "    ax.legend()\n",
    "\n",
    "    ax.set_xlim((-symlim, symlim))\n",
    "\n",
    "\n",
    "net.model.load_state_dict(last_state_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'SNR (dB) density: Total parameters: 2395210')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "\n",
    "# mkdir('snr_weights')\n",
    "SNR_vector = net.get_weight_SNR()\n",
    "np.save(results_dir+'/snr_vector_'+name+'.npy', SNR_vector)\n",
    "\n",
    "print(SNR_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(10*np.log10(SNR_vector), norm_hist=False, label=name, ax=ax)\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('SNR (dB) density: Total parameters: %d' % (len(SNR_vector)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'SNR (dB) CDF: Total parameters: 23928000')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0Gn7qgNsytvG7MO0sF07smEXqPNemWSoMRLYEPk0bFfMQvqntiIxtHIH/XqQ3Kd+Hv2hpvbgLzxMHA7NSTdjOz4/0BnBYeuf6sK/RanR9OoQ+Rc03fdvjZvYxvvPemyw7+ZyCn+Qn21VFK+fcXDO7Ht95LS/OuWYzOxt/ojoRuLCD7M+Ez1vTtgr7Nnzz0m1m9nN8M8Me+FEEuWxuZqkhfyvj26vXAy53zjVk5N0a+Hf6MMoc72W2mR2Pr17+Z3gsXmdZp97x+KucB9NWqzWz1MmvFh8A7YvvE/AsfihyQcKTdyPwlHOuo2NQiN/h+1I8EXaA/BQ/NPFw/MyVH4f5Psdfpf/IzF7AN3F84Zz7MKz+Ps7M5uI/X1vhq9A7ambLx0gz+xN+vpvh+P4WXxGO+nHOfWZmr+DnwvgGX6Wf6kya1/wjzrlEN5Y/c1+Pm9md+D5OV+ObiurxP0g74GdX/kOY/VXg+2EQ+xrQ4pz7F77j7sH4zq1X4JtW4/iZTL8P/DHsuJuTmV2KryGZhf9/D8PXhDbg+zWl8r0IrOec6zA4M7Nd8N/zufj/zWZmlp7l1VSwYX7+kOfD97cA3w/oaHwT3JEZ2z0H39/mY3xt3k/w3/0fZzTRnodvfr4XP5dIetARD2vPcM69EfaRO8n8UOsn8c3Nx+C/x639uZxzn4bH6TTzw5VTHXPPANLnKAHf/Dce31x9Nj5oOgn/fz2Ytk7FBzx3mNnN+P/b7/C1NzPaH93KpZqSvu1CfPXsFHyE/yh+KOJMYMscI1OybSNXW3hWzrm7gb/jR3Fkm8E0le8j/NwS+2QsX4rviDkT34v+L/gv8UEd7PYx/BDo2fgTeCowOTU9k/lZGHfGDxHN573chD9x/hN/YnoCPyrgYHy7/PiMVdZOK8eD+BEYDfgRIWNdlmGSeQjwfRRKNvOj83N5bIMfFnwp/vOxAzDZOXdGWr4ky64aH8GfRFP9kCbgfxB+ie8TsAt+qGrWdv8CTMF/bm8DpuJr3nZMC5TAz1fxd/xw0r/ga/92xv/Y56u7yp/NoSybVflu/A/UufgLw/TRHxfjPzeX4/s7/B+0Nrv+AP9+D8F/Bu9lWQ1CPh22X8D3a7oU/zm+Dt8ktqNz7r20fDHyu2DdFV+Tui7+x3t2xiO9s/Dz+ObLW/Df69/jb8PwA9d+YrUBwK/w3+lr8TVa22Xp95Kqpdwvy75nZeQdh6/x3QN/3Kbh+4gc4Jy7IiPvz/F94w7DBzDH4P9Xp6Vncn704k74/9NU/P9kELCLc+6ljLyP4s9z6+C/R5fhPwO75dvcVSmCZFIdf6X3mNn++BE8a7q0Sa+6cX9H42uIVu+spkR6li2bpGx359xjvV0eEel5qimR3nYv/ur7rO7eUTiU8gz8TbgUkIiIlBkFJdKrwuaBY4FP03rAd5fV8XNvXNbN+xERkSKo+UZERETKgmpKREREpCwoKBEREZGyoKBEREREyoImT2trEf423tnukyEiIiK5rYKf6LHoWZHV0bWt+mQyWZNI6JiUUiQSoGPas3TMe4eOe8/TMe95uY55JBIQBEEDXbghp2pK2voskUiOXLCg3W0YpEixWIQhQwawePFSWlo6uuu4lIqOee/Qce95OuY9r6NjPnToAKLRoEstDepTIiIiImVBQYmIiIiUBQUlIiIiUhYUlIiIiEhZUEfXIiUSceJx3XG6M4lEQENDlKamRuJx9ZDvCeV4zKPRKJFItLeLISJlTkFJgZLJJIsXL6C+fglQHif8cjdvXoREQj3je1L5HfOA2toB1NUNJQiC3i6MiJQpBSUFqq9fQn39NwwcOJjq6hpAJ9jORKNB2VyxV4ryOuZJGhsb+OabRVRVVdO//8DeLpCIlKleD0rM7NvAqcDWwGjgTefc6DzXPRWYCIwAXgVOc849001FJZlM8s03i6ipGcDAgSt01276nFgsojkEeli5HfOqqmpaWpr55ptF1NYOUG2JiGRVDh1dNwR+CLwDvJHvSmFAchFwDbBHuP6jZrZRdxQSIJFIkEjEqanp3127EOmzamr6k0jEy6xZSUTKSTkEJQ8651Z3zv0IeCWfFcysGjgHuMI5d6lz7mngUGAu8PPuKmgi4Tu2qsOeSOFS35vU90hEJFOvByXOuWIum7YFVgBmpG0nDvwJ2MPMurVuWFXPIoXT90ZEOtPrfUqKtH74/GbG8jeAQcBqwMfFbjwWyx6rJRI6qRYq9TsUBKB7P/aMcj/m0WiQ8zu2PItGI22epfsVcsw7iomTya6ll2IbPbWPruruz/nyGpQMARqdc/UZyxeGz0MpMiiJRAKGDBmQNa2hIcq8eZGcJ9XeuBAs9kN20003MG3aja2vq6urWXXV1dhnn3EceOAhJb+q7YkTdU++p4ceeoALL/xl6+tYLMaIEauw6667cfjhR9OvX7+S7Qvg008/Zb/99uSiiy5m7Njv57VOuf04JhIBkUiEFVboT01NTW8Xp9vU1RV9g1QpUmfH/JulTSxtaMmaFgCRDkardZZeim30xD4A+tfEGNi/NOem7vqcL69BCWSfJCToIC0viUSSxYuXZk1ramokkUgQjyfbjWyIAw2N2T/03ammOkYxPVwSiSTV1dVceeUNADQ2NvDSSy9yxRWXEQQR9t//wJKULwj8j2M8nuj2q/aeek+pfQFcdtnVDBgwkObmJl5//VVuvvkGliypZ+LEk0q2L4B4PNH63Nmomp485oWIx5MkEgm++mop9fV9r19JNBqhrq6WxYvrW/9f4nXl6r45nmRpjnNrNBKhtraKpUubSSSzH/NIxP9Quw8W0Jzlu9O/JsbqI+p475NFNDcXnl6KbfTEPqpiEdZbaygtTc1dOi909Dmvq6vt8sXQ8hqULARqzKzGOdeQtnxwWnrRcp30c0aoQUBDQzNvvJ/9Q99dqmIRNlhrKANrqkgW8SmLRCKMHr1ssNLmm2/BnDmv8+yzs0r2A54qVk/9OPbEe0pntj6DB/uP3Xe+szkffvgBzz47q+RBSSGKOeaNjY1UV1d3T4EyZAvq+5J8Ase+pLMayJZkMucFWxBAv34xGnOkRyIBLYkkb+Y4tw7qX8XIbw3hrQ/m09CYPdDtXxNjzVXqaGxK0Njcfj/RaEAymaSxsbj0UmyjJ/YRT0RJJpK0tCSL+r1ot71u+pwvr0HJnPB5feBfacs3AL4GPunxEgHNLQmampfvK8D+/fuzePHi1tfXX381s2c/x2effcqAAQPZZJPvMGnSyQwfPhyAu+++i6lTr+H++x9jwIBlk2J9+OEHHHLI/vz2t5ex005jAHjhheeYPv0m3n33Hfr3r2WnnXbmhBNOorbWVwO2tLQwdeq1PP30kyxYMJ+6ujrMNuDccy9g4MDiJ9zKfE9HHXUoa601knPPvaBNvqlTr+Whh+7nvvseIRYr7qvRv/8A4vG2J4QZM27nqaee4KOPPqCqqh8bbLAhEydOYY011myT77XX/su0aVN5/fXXSCaTrLXWSMaPP54tttg6677eftsxZcpEtt56W84661z+859/MXnycVxyyRU89ND9/P3vsxkwYAD7738ghx12VOt606ZN5a67bufKK6/nyisv4+23HccccxyHHHIYixd/xXXXXcVzzz3L0qX1jBo1imOP/RlbbrmsDBMnjqd///6MGfN9pk+/iXnz5rHBBhty+ulns8YaaxV13GT5FAcaGppzpncWVKQChnc/+oqmlvbnzlR6S0sy67m1uSUaPuc+9/arKq9mTOnY8hqUvAB8BRxIGJSYWRT4MfCIc66MKq3LW0uL/wFNNXX8/e+zOf74ya3pCxcu4Kc/PZLhw1dk0aKF3HXXHUycOJ7bb/8zsViMXXfdg+uvv5onn3ycfffdv3W9hx9+gGHDhrH11t8DYNasmZx33tnsscdeHH30BObPn8cNN1zD118v5vzzfwPAH/84nb/+9R6OP34SI0euzVdfLeKll16kubmppO9p77335aqrLmfKlNMZNGgQAPF4nMcee5jddvthQQFJIhGnpaWF5uZmXn/9VR5//BF23/2HbfJ8+eX/2H//H7PyyiNYunRJ+B6PYsaMe6mr85Pw/fe//+bEE49nww034owzzmHQoEG8+eYb/O9/n2fd72uv/ZdTTz2RXXbZjZNPPr3N1erFF1/ED36wK3vvfQkvv/wSN954HXV1dey7749a8zQ3N/OrX/2CH//4ECZMOIFBg+qIx+OccspkPvnkYyZMOIGVVlqJ++67h9NOO5HLL7+WzTb7buv6zr3JJ598zHHHTQLgppuu4+STJ3HnnfeUvD+NlKd8aog7CypSAUOuoEIBReXp9aDEzPrjJz8DWBOoM7PU2fNZ59yXZvYUsKZz7tsAzrlGM7sQuMjMvsTPb3IMsDZwUM++g+VXfX09O+3U9ip8jz324oADlh3Cs88+r/XveDzO6NEbM27cHrzyystsueXW1NXVMWbMWB5++IHWoGTZD/yexGIxkskk1157JWPH7sKZZ/6idXtDhw7l9NOncPjhx7D22qOYM+d1ttxyK/bb74DWPDvttHPJ39Muu+zGNddcwcyZjzNunP+ovfTSbL788gt++MO9C9rf3nvv2ub1lltuw4QJE9ssmzz5lNa/4/E4W2yxFXvu+QNmzXqKffbZD4Drr7+K1VZbnSuvvJ5oNBpuK3sNycsvv8RZZ53C/vsfyHHHTWyXvtlm32XSpCm0tCTYaqttWLBgHrfdNp29996PSMSf5FtaWhg//oQ2nWafe+5Z5sx5nUsuuYJtttkOgK222pbDDjuQW265sU1QsnDhAq655kZWX30NANZZZ11+8pMf8eijD7W+J+kbcjXPpBarlkJKqdeDEmAl4O6MZanXY4BngCjty3oZvmPrZGBl/DTzezjnXu22kvYx1dXVXHvtTQA0NTXh3JtMm3YDsViM00/3c9DNnv08t946jblz32XJkiWt63700QetP5p77TWOiRPH895777L22qN48cUXmD9/XusP/IcffsDnn3/G5MmntNZiAGy66eYEQYBzc1h77VGsu+563HnnH5k2bSrbbrsdZuu3/oiW8j0NGDCQsWN34eGHH2gNSh5++AE22mhj1lprZEH7u+KK6xg4cCAtLXHef/89br75Bs4++zQuvfTK1pP5a6+9ys03X89bbzkWL/4q7Rh+CEBDQwOvv/4aEyac0BqQ5DJ79vPMnPkEhx9+FIcffnTWPDvsMCbj9Vgef/xRvvjiC0aMGNG6fJttvtcm33/+82/69x/QGpCA76MzZsz3+eMfpxOPx1vLN3LkqNaABGD11ddg5MhRvP76qwpK+pCOmmcikYDK6TkjPaXXgxLn3Pt0clc759xOWZYlgUvChxQhEomw3nobtL7eeONNaWlp4dprr+BHPzqIxsYGzjzzZLbffkcOPfRwBg/2d3idMOEIGhuXNalsuulmrLHGmjz88P1MmnQyDz10P5ts8p3WPhOLFi0C4OyzT81ajlQTxWGHHUUQBDz22MNMn34TgwcPYb/9DuDII4/NezhvZ+9p7bVHAbD33uM47rijeOedtxk+fEWef/7/OOWUMws4et63v71ua0fX0aM3YuDAgZxzzhnMnv082267HZ9//jknnzyR9dZbn9NOO4vhw1ekqqqK0047iaamRgC+/noxiUSC4cNX7HR/zz33N6qrq9lll91y5hkyZEjW1/Pnz2sNSmpqalr78qR8/fVihg4d2m57w4YNo6Wlhfr6+ta+PZn7SC2bP39+p+9Blg+dNc+kmmYC3ZRUSqjXgxIpL6magrlz3+Wdd95m4MCB/OpXv22tsfj888+yrrfnnvty5523cdBBhzJ79nOcccY5rWl1dXUATJlyOhtu2P5ei6kf4379+nH00RM4+ugJfPzxRzz88APccsuNrLrqauy22w/brVfMe0oFJaNHb8zIkWvz8MMPMGLECGKxKsaO3aXofSzb19oAvPfeO2y77Xb8/e8vUF+/lF//+pLW/istLS1takwGDhxEJBJh3rwvO93+pElTeOCB+zjxxJ9x7bU3stJKK7fLs3Dhwqyvhw0b3rosW5BXV1fHggUL2i2fP38+sVisTRCTuY/UMrP1On0PsnxRfw/pSfpUSRtz575K34RfAAAgAElEQVQLwAorDKaxsYFYLNbmB+yJJx7Nut7uu+/JkiXfcP7551BdXc2YMcv6Kqy11khWWmllPv30E9Zbb4N2j2w1BN/61upMmHACdXUr8MEH75fsPaXba69xPPnkozz44P3svPMu9O/f9Rstvvee31eq9qSxsZEgCNp0nn366ZnE48tO8rW1tWy44UY89tjDbZZnU1NTw6WXXsngwSswefLxzJ8/r12ev/1tVsbrpxk+fEVWWmmlDre98cabsnTpEl588YXWZYlEglmznmL06I3bNC3Nnftua/MT+KaouXPfZYMN8rrBt4hIVqopqWCJRILXXvNdcFpamnFuDrfeOo211lqbTTfdjObmJv785xlcfvnF7LDDGF577b88/vgjWbc1ZMgQtttuR2bNmsnee49rM2NnEARMnDiF88//OQ0N9WyzzXbU1tby+eefMXv2c4wffwJrrLEmZ511Cmbrs846Rm1tLc8//zcWL/6qTQfLrr6ndLvttgc33HANixa9x5lnnpNtc51ybg4DBgwkHo/zwQdzmTZtKkOHDmvt17H55lsAcNFF57PPPvvx/vvvMWPG7QwcOKjNdo47bhInnngcJ530M8aNO4BBgwbx1ltvssIKg9lzz33a5B0wYCCXXXYNkyf7/FdffWNrEATwyisvc/XVl7P55lvxj3/8nccff5STTz6j0/4522yzHeuvvyEXXngu48efwIorrsT999/DRx99wMknn94m75AhQznzzJM55pjjSCbh5puvZ/jwFdl99z2LOo4iIqCgpKSqevh+Hl3dX2NjI8cddyQA0WiUlVYawQ9+sAdHHXUssViMbbbZjuOPn8Q99/yZRx55kI022oSLL76Cgw/O3pFxhx12Ytasme1+RAHGjv0+gwYN5NZbb2mtbRkxYhW22mpbhg4dBsBGG23C00/P5K67bicej7P66mty3nkXssUWW5XsPaWrq1uBTTf9Dl988T9Gj944732kO+UUPyQ2EokwfPiKbL75FhxzzHGtQ31Hjfo2Z511LtOn38Tpp09hnXXW5cILf8cvftG2/8omm2zK1VdP5aabrueii35JJBJl5Mi1OfbY47Put66ujssvv5ZJk8YzZcrPWmexBTjttLN54IF7ueeeu+nffwDHHHNcmxFNuUSjUS677CquvfZKpk69hvr6ekaN+jYXX3xFu8DQbD123HEs1113FfPnz2ODDUZz6qln9dgEbCLSNwWlmNmtD3kvHk+MXLBgSdbE5uYm5s//jGHDVqGqqu1cDMvbNPPd4YILzuXttx233fanNstjsUhZznC5ZMk37LvvHhx11HgOPvjQ3i5Ol73yystMnnwcN998G6NHj+62Y56aPO3ii68oaL2Ovj99QSwWYciQASxcuKQsP++FCoKAbxqa+c8787L2KRnYv4pR3xrMm3MX5pyFtLM8XU2vG9CPddcaxuvvfkl9jnvbdHcZlpd99KuKssm3hxc9A3hKR5/zoUMHEI1G5uKn5yhu+0WXTNqIAgNrqnp8v+UQVL777ju8/bbjqaeeKGoES09bunQJc+fO5b777iYIAn74w716u0giIoKCkpIqhwChN5xxxhQWLVrI7rvvWfDkY/mKx+MdHt9CZmF98805TJ58HCuttDI///kvW5taUhKJBIlE7ivdaDRa8rsoi4iIghIpgb/85cFu38eJJx7Pv//9Ss70u+9+gFVWWTWvbW222Xd57rmXc6b/5je/4tFHH8qZftVVNxTU+bandPa+SuWaa27s9n2ISGVSUCLLhdNPP5ulS5fmTM9n4rF8HXXUePbf/8c50zNvpCciIqWhoESWCz1599lVVlk171oXkeVdZ/e2EelJCkpERCqU7m0j5UZBSREqtUOrSFfoe1NedG8bKUcKSgqQmma7qamRfv00SZRIIVI3IIxGddopJ7q3jZQTnR0KEIlEqa0dyDff+JuR9etXraGheUgkAuJxXSX3pHI65slkkqamRr75ZiG1tQM7ne5eRCqXgpIC1dX5W7unAhPpXCQS6XDeDym9cjzmtbUDW78/IiLZKCgpUBAErLDCMAYNGkI83vPTyi9votGAFVboz1dfLS2bK/e+rhyPeTQaUw2JiHRKQUmRIpEIkUjfu39HqcViEWpqaqivj/eJ+4EsD3TMRWR5pUsXERERKQsKSkRERKQsKCgRERGRsqCgRERERMqCghIREREpCwpKREREpCwoKBEREZGyoKBEREREyoKCEhERESkLCkpERESkLCgoERERkbKgoERERETKgoISERERKQsKSkRERKQsKCgRERGRsqCgRERERMqCghIREREpCwpKREREpCwoKBEREZGyoKBEREREyoKCEhERESkLCkpERESkLCgoERERkbKgoERERETKgoISERERKQsKSkRERKQsxHq7ACIi0j2CIOggrQcLIpKnsghKzGxd4Cpge2AJMAM40zlX38l6A4BfAAcAqwCfAHcAv3HONXZroUVEylgcaGhozpkeiQQkeq44Innp9aDEzAYDTwMfAPsDKwG/B4YBh3ay+vXAvsDPgdeALYELgKHA5G4qsohIWQuCgIaGZt54fwHNLdlDj/41MdZcpY4AVZlI+ej1oASYAAwBNnXOzQMwsxbgDjP7tXNuTraVzCyGryG52Dl3dbh4lpmtCRyIghIRqXDNLQmamuNZ0/pVqUuhlJ9y+FTuAcxMBSShe4DGMC2XAB9UfZWxfFGYJiIiIsuRgoMSM7vazKyEZVgfaFMbEvYHeTdMy8o51wxMByaZ2VZmNtDMxgDHAteUsHwiIiLSA4ppvjkM+JmZPY3/8X/AOZfsQhmG4Gs3Mi3E9w3pyPHADcCLacuuds79qgvlIRYrhwqkviEajbR5lu6nY947yum4BwEEkYBo+MgmEgQEQUAkCtF4+zxdTe+JfUTC9xYJ+vb7LMU2opGAIBIQiwUkk8U3JnT357yYoGRV4HDgZ8B9wEdmdj1wc0YTTCGyBTVBjuXpfgvsCYwHHLA5cL6ZLXTOnVdMQSKRgCFDBhSzqnSgrq62t4tQcXTMe0e5HPemxFJqa/sRq8re0bW2JkYsFqW2ph+xWPs8XU3viX1UV8fC5yoi0WiffZ+l2EZVLEJtTT8GD+6fdfuF6q7PecFBiXNuCXAdcJ2ZjQUm4ke8nGdmfwKucc69XMAmF+JrSzINJqNZJ52ZjQZOBfZxzj0QLv6bmSWAS83sWufcFwWUA4BEIsnixUsLXU1yiEYj1NXVsnhxPfG4BiD2BB3z3lFOxz0IoL6hmfr6ppwdXYNkgpaWOPUNTTQ1tc/T1fSe2Ec08NetjY3NNDS29Nn3WYpt9KuKUt/QxKJFSZJdaNvo6HNeV1fb5RqULo2+cc49DTxtZt8CbgV+CvzUzF4GLnTOPZjHZuaQ0XfEzKqBUcAtHay3Qfj874zl/8a/rzWBgoMSgJYcQ+ikePF4Qse1h+mY945yOO5BEJBMJImHj2wSySTJZJJEnKx5upreE/tIhMsSyb79PkuxjXgiSTKRpKXF5+uq7vqcdymkMbNaMzsGeBAYgw8wzgeiwF/N7Bd5bOYRYGczG5a2bBxQHabl8kH4vHnG8u+Gz+/nsW8REREpE0XVlJjZKOAE4AigDngUOM05NzPM8iszuwiYhG/a6cjUMN/9ZnYByyZPuyN9jhIzmwYc7pxLlfll4CXgBjNbGd+nZAvgXOBPzrkvi3lvIiIi0juKGRL8KD4AOBq4DTDn3F5pAUnKg8DwzrbnnFsEjMVPL38vPiCZgR/amy4aPlLrxYG9gL8CZ+BrVY4Brg6fRUREZDlSTE3JKGAKMN05900H+V7DN+l0yjn3FrBrJ3mOwNfMpC/7Aj8jrIiIiCznihl9s26e+b4Gni24RCIiIlKRimm+iZvZljnSNjez7OOZRERERDpQzOibjqaCi9D5hGciIiIi7RQ7JDhX4LE57W+QJyIiItKpvPqUmNmJwInhyyR+DpLGjGy1+OG8fyld8URERKRS5NvR9Qvg9fDvtYD3aH8TvUbgVeDKkpRMREREKkpeQYlzbgZ+7hDMbBZwvHPuze4smIiIiFSWYoYE5zX3iIiIiEgh8u1TsgbwmXOuOfy7Q865D7tcMhEREako+daUzAW2wd9r5n06H/Yb7SRdREREpI18g5KjgHfT/tZcJCIiIlJS+XZ0vTXt7z90W2lERESkYhVzQ752zKwGP1T47fDuvSIiIiIFKebeN5PM7BdprzcHPsLPY/KWma1ewvKJiIhIhShmmvljaDtx2u+ABcAU/H1xzilBuURERKTCFBOUrAG8CWBmg4AdgLOcc1cB5wE/KF3xREREpFIUE5RUA83h39uE25gZvn4fGNH1YomIiEilKSYo+RDYPvx7H+DfzrnF4esVgcVZ1xIRERHpQDGjb24HzjOzfYFNgFPT0r4LvFWKgomIiEhlKSYo+TXQAmwL3AdclZY2GrinBOUSERGRClPMDfmSwG9zpO3d5RKJiIhIRSqmT4mIiIhIyRU1o6uZHQocAqwJ1GYkJ51zo7paMBEREaksBQclZnYG8BvgDeA/QGOpCyUiIiKVp5iakvHAtc65SaUujIiIiFSuYvqUjMCPuhEREREpmWKCkn8C6jMiItLLgiDI8ejtkokUp5jmm5OB283sFefcP0tdIBER6VwcaGhozpoWiQQkerY4IiVRTFAyHRgGvGRmnwPzM9KTzrlNulwyERHJKggCGhqaeeP9BTS3tA8/+tfEWHOVOgJUZSLLl2KCkvnAvFIXRERECtPckqCpOd5ueb8qTUEly6diZnTdqRvKISIiIhVO4bSIiIiUhWJndF0R3+F1J2A4sK9z7nUzmwC85Jz7V+mKKCIiIpWg4JoSMxuJn8l1MpAE1gaqw+SNw+UiIiIiBSmm+eZiYBGwDrADtOne/RzwvRKUS0RERCpMMUHJzsD5zrlP8TUl6T4DVu1yqURERKTiFBOU1AALcqQNAM3ZIyIiIoUrJihxwPdzpO0AvFZ8cURERKRSFTP65ibg92b2KXBHuKyfmf0I+BkwsVSFExERkcpRcE2Jc+464DbgcuDzcPFzwJ+AO5xzt5aueCIiIlIpipqnxDk33sxuAfYEVsJPO/+Qc+6FUhZOREREKkdRQQmAc+5F4MUSlkVEREQqWMFBiZmtDmzPsqG/nwJ/c859XMqCiYhUuiDIfpffHItFlnt5ByVmNgi4ETgAP2Fa+tciYWZ3Asc755aUtogiIpUnDjQ0NGdNi0QCzb0gfVJeQYmZBcBD+BqSR4AHgA/wgcmawD7AocBq+MnVRESkSEEQ0NDQzBvvL6C5pX340b8mxpqr1BGgKhPpW/KtKdkPH5AcmWN0zVQzOxKYZmbjnHP3FVIIM1sXuCrcxxJgBnCmc64+j3WHAhcC44AhwIfAZc65qYWUQUSk3DS3JGhqjrdb3q9KN3iXvinfoORA/OianMN9nXPTzWwccBCQd1BiZoOBp/E1L/vjR/P8HhiGr33paN2BwLNAPXAi8AX+njxV+e5fREREykO+Qcl3gN/kke+vwFkFlmECvoZjU+fcPAAzawHuMLNfO+fmdLDu2UAtsGVarcozBe5fREREykC+dYArA3PzyPdemLcQewAzUwFJ6B6gMUzryFHAtHyaeURERKS85VtTMgDf16Mz9WHeQqwP3JK+wDnXaGbvhmlZmdlIfAC00MweAnYBvgHuAk7tSqASi6m9tlSi0UibZ+l+Oua9o5THPQggiAREw0emSBAQBAGRKETjhaeXYhvlsI9IeGwiQfbj1FfeZym2EY0EBJGAWCwgmSy+g3R3n1/yDUoCINktJfBNN4uyLF8IDO1gvRHh8yXA3fhalQ3wzUz9gGOLKUwkEjBkSKFxlXSmrq62t4tQcXTMe0epjntTYim1tf2IVbUffVNbEyMWi1Jb049YrPD0UmyjHPZRXR0Ln6uIRKN99n2WYhtVsQi1Nf0YPLh/1u0XqrvOL4VMnnanmXVW+1BsKbMFPJ0FQqkwbY5z7qjw76fMrAq4xMx+4Zz7PMe6OSUSSRYvXlroapJDNBqhrq6WxYvricc1s0JP0DHvHYUc95ZEkqWNLTnTI5GAeDxJfX1T1tE3QTJBS0uc+oYmmpoKTy/FNsphH9HA/0Q0NjbTkON49oX3WYpt9KuKUt/QxKJFSZJdqGLo6HNeV1fb5RqUfIOSv5F/TUmhM7suxNeWZBoMdNTJdUH4/HTG8qfxAcv6LLthYEFasswLIF0Tjyd0XHuYjnnv6Oy4B0HAkg7mIIFl85AkEhBPtD/1JpJJkskkiXhx6aXYRjnsIxEuSySTffp9lmIb8USSZCJJS4vP11XddX7JKyhxzu1U8j0vM4eMviNmVg2MIqOvSYZ3gaYsy1ONZTobi0jZyjUHCWgeEqlc5fDJfwTY2cyGpS0bB1SHaVk555qAJ2k/g+zOQAvwRonLKSIiIt2o6LsEl9BUYBJwv5ldwLLJ0+5In6PEzKYBhzvn0sv8K+A5M7sNuB3f0fV84Brn3Jc99QZERESk63q9psQ5twgYix9yfC8+IJlB+9Ez0fCRvu5LwA/xwciDwOnA1eGziIiILEfKoaYE59xbwK6d5DkCOCLL8ifxzTgiIiKyHOv1mhIRERERUFAiIiIiZaLo5hszWw/YERiOv//M52a2KrBQ96IRERGRQhUclJhZFLgR378jNevqo/iJyqYC/wLOLV0RRUREpBIU03zzc+AQ4DRgNMsmKwMfnOxWgnKJiIhIhSkmKDkCuMA593vAZaTNBUZ2tVAiIiJSeYrpU7IaMDtHWgMwqPjiiIj0DUGQ/fbwORaLCMUFJV8AawOzsqQZhd+QT0SkT4kDDQ3NWdMikUA35hLJoZig5BHg52b2GMvuwps0sxWAyfiZVUVEKlIQQENjS867AKfuABygKhORTMUEJecCu+NveDcLP/rmInyn12bggpKVTkRkOZXrLsC6A7BIbgV/O5xz/wO2wN+fZnN8TeUm+JE32zrnFpS0hCIiIlIRipo8LQxMjitxWURERKSCFVxTYmYTzWxIdxRGREREKlcxjZtXAZ+a2Z/MbFczU28tERER6bJigpL18YHJ9/AjcT4ys1+b2TolLZmIiIhUlGI6ujrn3BnAGsBewAvAycCbZvZ/ZnZkicsoIiIiFaDosWnOuYRz7hHn3I+BVYBJwJrATaUqnIiIiFSOLg+YN7M64MfAT4Fv4aeaFxERESlIUUOCAcxsZ+BIYBxQC/wdmADcVZqiiYiISCUpOCgxs/OBw4HVgf8B1wDTnXNvlrhsIiIiUkGKqSk5E3gImAg86pxrP4+yiIiISIGKCUpWc87NK3lJREREpKIVMyRYAYmIiIiUXF41JWZ2C3CBc25u+HdHks65o7teNBEREakk+TbfjAGuDP8eCyQ7yNtRmoiIiEhWeQUlzrmRaX+v1W2lERERkYpVzF2C1zCzqhxpMTNbo+vFEhERkUpTzIyuc4Hv5EjbJEwXERERKUgxQUnQQVoU9SkRERGRIhR775t2gYeZVQO7AxoyLCIiIgXLd0jwecC54csk8KKZ5cp+cwnKJSIiIhUm3yHBLwHX4Ztufgb8BX/fm3SNwKvAnSUrnYiIiFSMfIcEPwo8CmBmA4BfOefUoVVERERKpuB73zjnjuyOgoiIiEhlK+aGfACY2WhgfaA2M805d1tXCiUiIiKVp+CgxMz6Aw+wbLr51BDh9BE5CkpERESkIMUMCf4FsBawIz4g2Q/YBbgXeBvYrFSFExERkcpRTFCyD/A74IXw9YfOuaeccwcArwDHl6pwIiIiUjmKCUrWAt50zsXxTTb909LuAPYtQblERESkwhQTlCwCBoR/fwGsk5ZWlZYmIiIikrdiRt+8CqwLPAbMAs42s7eBJvysr/8pXfFERESkUhQTlExjWe3Iz4HngGfD14uAPUpQLhEREakwxUye9ue0v+ea2bosGx78gnNuQQnLJyIiIhWi6MnTUpxzS4AHS1AWERERqWBdDkpKIaxtuQrYHlgCzADOdM7VF7CNcfi5Ul53zo3uloKKiIhIt8krKDGzBG1nbO1I0jmXd7BjZoOBp4EPgP2BlYDfA8OAQ/PcRm24Tuadi0VERGQ5kW/w8CvyD0oKNQEYAmzqnJsHYGYtwB1m9mvn3Jw8tnEW8CEwF/huN5VTREREulFeQYlz7pfdWIY9gJmpgCR0D3BLmNZhUGJmo4BTgG2BKd1VSBEREelexUyeVmrrkxF4OOcagXfDtM5cCdzmnNP8KCIiIsuxYu4SfFhneZxzhdwleAh+fpNMC4GhnZRlL3wNyboF7K9TsVg5xGp9QzQaafMs3U/HvHekH/cgEhANH5kiQUAQBESiEI23T88nT1fT+8o+IuHxjQTZj3VfeZ+l2EY0EhBEAmKxgGQy+z7y0d3nl2JG3/whx/L0PieFBCWZ66YEOZYDYGY1wBXAeRlNP10SiQQMGaKZ8kutrq62t4tQcXTMe8egQbXUNyepre1HrCrRLr22JkYsFqW2ph+xWPv0fPJ0Nb2v7KO6OhY+VxGJRvvs+yzFNqpiEWpr+jF4cP92acXorvNLMUHJyCzLhuPvHnwgcFCB21uIry3JNJiO+5OcBCSAGeEIHoB+QCR8vdQ511RgWUgkkixevLTQ1SSHaDRCXV0tixfXE49n/7JJaemY947Ucf/663rqG5qor2+iqTneLl+QTNDSEqe+oYmmpvbp+eTpanpf2Uc08NetjY3NNDS29Nn3WYpt9KuKUt/QxKJFSZJdGLbS0fmlrq62yzUoxczo+kGWxR8A/zSzKuBE4IgCNjmHjL4jZlYNjMJ3ds1lPeDbwJdZ0hYCxwM3FFCOVi0tOpGXWjye0HHtYTrmvSMeT5BMJImHj0yJZJJkMkkiTtb0fPJ0Nb2v7CMRLksksx/rvvI+S7GNeCJJMpGkpcXn66ruOr+UulHoKWDvAtd5BNjZzIalLRsHVIdpufwWGJPxeBx4P/z7gQLLISIiIr2o1DO6rglkr3vKbSowCbjfzC5g2eRpd6TPUWJm04DDUxOzOefeBN5M35CZHQF8yzn3TLFvQERERHpHMaNvdsiyuBrYGD+J2VOFbM85t8jMxgJX46eJX4qfZv6MjKzR8CEiIiJ9UDE1Jc/QflRManzRTHytR0Gcc28Bu3aS5wg66asS5hEREZHlUDFByZgsyxqA951zuveMiIiIFKWY0TfPdkdBREREpLJpykcREREpC8V0dI3i+438BD/apiYjS9I5t0IJyiYiIiIVpJg+Jb/F35X338CTQMGzpoqIiIhkKiYo+QnwW+fc2aUujIiIiFSuYvqU1OKH/oqIiIiUTDFByRPAVqUuiIiIiFS2YppvJgMPm9kS/L1pFmRmcM61WyYiIiLSkWKCksWAAy4PH9loOngREREpSDFByQ3Aj4H7gTlo9I2IiIiUQDFByb7AWc65S0tdGBEREalcxXR0bQb+VeqCiIiISGUrJii5l07u6CsiIiJSqGKab2YAN5lZFfAw2UffvNLVgomIiEhlKSYoeSp8PhE/PDhdACTR6BsREREpUDFByZElL4WIyHImCIKM122fRaRwBQclzrlbu6MgIiLLizjQ0NDcZlkQCWhKLKWxqYVE7xRLZLlXTE2JiEjFCoKAhoZm3nh/Ac0ty8KPaCSgtrYfQTLBt1YeRICqTEQKVXBQYma3dJIl6Zw7usjyiIgsF5pbEjQ1x1tfRyMBsaoEQVL1JCLFKqamZCy+M2u6YcBAYFH4EBERESlIMX1K1sq23MzGAtcBB3SxTCIiIlKBipk8LSvn3NPANcCVpdqmiIiIVI6SBSWhN4AtS7xNERERqQClDkp2BOaVeJsiIiJSAYoZfXNulsXVwMbA7sAlXS2UiIiIVJ5iRt/8MsuyRuB94FwUlIiIiEgRihl9U+omHxEREZGS9ykRERERKUpeNSVmNgS4GZjunHsoR5498TfrG++cm1+6IoqIiEglyLem5BhgE+CxDvI8BmwEnNDVQomIiEjlyTcoOQi4yTnXkitDmHYTsHcpCiYiIiKVJd+gZF3g5TzyvRLmFRERESlIvkFJDGjOI18zUFV8cURERKRS5RuUfAZskEe+DYHPiy+OiIiIVKp8g5JngZ+ZWc5akDDteGBWKQomIiIilSXfoORyYD3gPjNbNTMxXPZXwMK8IiIiIgXJa54S59x/zewE4Dpgrpn9E5gbJo8ENscHOMc7517tlpKKiIhIn5b3jK7OuZuAHYAn8DffOzh8bIyfo2R759zN3VFIERER6fsKuveNc242sJeZRYDh4eJ5zrlEyUsmIiIiFaWYuwQTBiFflLgsIiIiUsF0Qz4REREpCwpKREREpCwoKBEREZGyoKBEREREykJRHV1LzczWBa4CtgeWADOAM51z9R2sUwecDOyOn7StGfgncLZz7pVuL7SIiIiUVK/XlJjZYOBpYBCwP3Aq8BPgpk5WXQOYAMwEDgSOBKLAC2a2WbcVWERERLpFOdSUTACGAJs65+YBmFkLcIeZ/do5NyfHenOBUc65pakFZjYTeA+YhA9SREREZDnR6zUlwB7AzFRAEroHaAzTsnLOLUkPSMJlDcAcoN39eURERKS8lUNNyfrALekLnHONZvZumJY3MxsAfAe4rSsFisXKIVbrG6LRSJtn6X465t0rCCCIBETDR0okEgmfA4IgIBKFaDxot34k6Dg9nzxdTe8r+4iExz8StP1f9LX3WYptRCMBQSQgFgtIJrPvIx/dfX4ph6BkCLAoy/KFwNACt3Uh0B+4ptjCRCIBQ4YMKHZ1yaGurra3i1BxdMy7T1NiKbW1/YhVtb/DRnV1FbFYlNqafsRi7dNra2IdpueTp6vpfWUf1dWx8LmKSDTaZ99nKbZRFYtQW9OPwYP7Z91+obrr/FIOQQlAMsuyIMfyrMzsEOAk4ATn3DvFFiSRSLJ48WVpE4wAABYHSURBVNLOM0peotEIdXW1LF5cTzyuWyT1BB3z7hUEUN/QTH19E03N8dblkUiEmpoqGhubaWmJU9/QRFNTvP36yUSH6fnk6Wp6X9lHNPA/EY2NzTQ0tvTZ91mKbfSrilLf0MSiRUmSef+yttfR+aWurrbLNSjlEJQsxNeWZBqM7x/SKTPbBZgOXOKcu66rBWpp0Ym81OLxhI5rD9Mx7x5BEJBMJImHj2X8sU4kkiSTSRJxMtLDXMmO0/PJ09X0vrKPRLgskcz8X/St91mKbcQTSZKJJC0tPl9Xddf5pRwaneeQ0XfEzKqBUeQRlJjZlsC9wN3AGd1RQBEREel+5RCUPALsbGbD0paNA6rDtJzMbP0wz/PAkc65rod/IiIi0ivKoflmKn5ekfvN7AJgJeD3wB3pc5SY2TTgcOdcLHy9EvA4fibXS4DNzSyVvdE596+eewsiIiLSVb0elDjnFpnZWOBqfDPMUvw085lNMdHwkbIBsHr498yMvB8Aa5W8sCIiItJtej0oAXDOvQXs2kmeI4Aj0l4/gx+hIyIiIn1AOfQpEREREVFQIiIiIuWhLJpvRESWV8lkkpfmfMHCrxuJRAKSySQrDq7lO+usSKAGZpGCKCgREemCLxbW4z5se6eM/y2op6EpzrajR/RSqUSWT2q+ERHpgk/mLQFg1eH92X2btRiz2WoEwDsff8XHX37Tu4UTWc4oKBER6YJPw6Dk26utwNqrrcCGI4ey/aarAvDi6/+jqSX7vUxEpD0FJSIiRapvbGHB4kYAVh2+7O7iu2+zJoP6V7G0oYVX3Je9VTyR5Y6CEhGRIqVqSYbVVVNbvayLXr+qKDtsshoAb330FZ8v0J3HRfKhoEREpEjL+pMMaJe26vAB/9/enQfHfdZ3HH//dlfS6r4P306c+LETp+RoAqHNcISG1OVICoUyhFIGCp0ppKVAGyjtFCgMMBQ6wNChtCSdTkiBCUcbQjEQriSEMGCCiZ3Hji/5kGTrsiTr2OvXP57fT1qvV/LualeS5c9rRrPyPs/vt4+f32r3+3tOrljfDMC+IyNLWi6Ri5WCEhGREmR8f7alZG3n+UEJwNYNLQD0D03OuyW9iMxRUCIiUoLB0SkSyQxVsQidzbV587Q31RCvjpJMZzg9MrXEJRS5+CgoEREpwfFTrpVkTXsdkUj+VdI8z5vt2gm7ekRkfgpKRERKEK5Bsi7PeJJss0GJ1iwRuSAFJSIiRTo7lZztjsk3yDXb2o46AEYnEpydTla8bCIXMwUlIiJFeqZ3BB9oaaimvrZqwbzx6hgdzXEATp5WF47IQhSUiIgU6Wj/OADdbXUF5V/XqXElIoVQUCIiUqRwMbTWxpqC8ofjTvo0NVhkQQpKRESK1D/kgpLmhuqC8rc3x6mpipJMZRjQ6q4i81JQIiJShKmZFCPjbr+blvrCWkrc1GDX1RN2/YjI+RSUiIgUoW/IjQuprYlRUx0t+LhwXEnvgKYGi8xHQYmISBHCpeVbGwvrugmtaXdBydCZaWYS6bKXS2Q1UFAiIlKEMChpaSis6yZUWxOjLthJ+MSgWktE8lFQIiJShBOzLSXFBSUAbU3umOOnFJSI5KOgRESkCLMtJSUEJe3BImonTmm9EpF8FJSIiBRoJpFm8Mw0AK1Fdt8AtDe5oEQtJSL5KSgRESlQ37Br4WiorSIejA8pRlsQlAyMTJJMZcpaNpHVoPi/KhGRS9SJYO+aNe2FLS+fqy7uBrtOzqQYHpumpcgZPKWYnE6x+8BpxieTpNM+6UyGeHWM600Hl69rrvjrixRDQYmISIFOBmuU9JQYlAB0ttZytH+cwTNTFQ9Kjg6M86PdJ5hJ5k5BTvDwT3vZc2iYqze10dkax/O8ipZFpBAKSkRECtQ36JaI72mrL/kcnS3xICiZ5or1lWmpSKczfP1HB3n0qT7AzRS65vI2YtEIkYhH39AktneEYwMTHBuYoD4eY/OaRjb3NNHWVKMARZaNghIRkQKF64usaa9jciZV0jk6W2oBGDwzVbZy5frVgUF+tvcUANs3tXK96SAamRtCuLajnhu3d3G4b5zHf93H2ekUTx8e4enDI1y5vpmbd/RUrGwiC9FAVxGRAswk0wyOupk3i+q+CYKSkfEZUunKDHY9dHIMgOdd3cON27vOCUhCdfEYr7jlMu56qeEF165lU08jAAeOn2GqxIBLZLEUlIiIFKB/aBIfN/Omobaq5PPU18ZoqK3C95nd2K+cJqdTnA6Cpy0FDGSNRSNs6mnkBdeupSNYR6V3QJsGyvJQUCIiUoBwkOu6jvpFjbnwPI/1XQ0ADI1Nl6Vs2cLF3TZ0Ncwua1+osLXkaL/WUZHloaBERCQPz/PO+Qm/7Nd2lD7INRQGJcNj5W8pOXHaBRTbNrcWfezGbleugeFJdeHIslBQIiKSIw1MTCfP+ekNVmHtaKllsSNB1nfN7RhcTpmMz8khN0No++a2oo9vrKumvakGHzgcjEsRWUqafSMiksXzPKank+w9Mjy76mrG9zlwbBSAtJ8hlc7gUXoXTthSMjoxQzqdIRotz/3hqdEpkqkM8eooG7oa2H90tOhzbOxpZGhshoMnFJTI0lNLiYhIHslUhkQyTSKZ5sSpCaYTaaqrIrOzZxajpaGGmqoovg9DZezCCVec3djdQCRSWtC0qduNKzl+eoKzU8mylU2kEApKREQu4FjQdbO+s4FoiV/22TzPY32n68I5dPLMos8XCseThANWS9FUX01rYw2+D08fHi5X0UQKoqBERGQBvu/TO+C+7DcE3S7lsG2TG/Nx6ORYWTbnm5hKMjqRwGPx5QyDmqcODC66XCLFUFAiIrKA0YkEE1NJohGvLDNvQmva62iqqyKV9jnct/jxGyeDrpuOlnhJOxhn2xTMwjlwbJTh8fJPWxaZj4ISEZEFhAuJremopypWvo9Mz/O4ckMLAPuPjeL7fsnnmpxO8ewJ1w20rnPxrTnNDTV0t9WSzvh88yeHyxI0iRRCQYmIyALC8SQby9h1E9qyrolIxGN4bKakhdQSyTS795/m6z8+xOCZaSKeN9vKsVg7b97EFeubSaUz/OSpPn6+7xSZTOmBk0ghFJSIiMxjYirJ8NgMHnNri5RTvDo2G0TsP1bcgNffHBziy48cYM+hYdIZn86WOLfdtIHmhpqylK22JsZb79jBc67oAGDf0RG+/UQvY2cTZTm/SD4KSkRE5nEsGODa1VpLvLoyyzptDbpwjvSNkUimL5g/mcrww1+e4N5v7WM6kaapvpoXXreW25+7ka7WxU9XzhaNeNy0vZsXXreW6qoIQ2PTPPT4kUV3N4nMZ0UsnmaM2Qp8GrgFOAs8ANxjrb3g3t7GmDcC7wU2A88CH7DWfrVypRWRS0XYdbOhTF0i+XS11tJcX82Zswke/00/Oy5vp6HObfg3OZPiSN+Z2R2FU2mfUyNTTEwl8YBrtrRzzZa2vLsAl9PG7kbam+M89ut++ocneeLpAXoHxnnp8zJEFrGInEiuZQ9KjDEtwCPAUeBVQBfwSaAduOsCx74auA/4KLALuAP4sjHmjLV2VwWLLSKr3LGBcQZG3JLt5ZwKnMvzPHZc3sZje/rpHZigd2CCzpY48erYbFCUq742xhtu30Y65TOTXJo9aurjVfzejevZe2SE3ftPc3Jwknsf2kdjXRXbNrWydUNLWdZwkUvbsgclwNuAVuBaa+0ggDEmBdxvjPmwtXbfAsd+CPiqtfa9wb9/YIzZBnwQF6SIiJxnoV1+PQ8e29PHrp8fw/fd6qiNddUVLc+Wdc001FWxv3eUowMTnB6dG/Ta3lRDV2sd1VURYtEI1VVRtm9u5coNLTxzeKSi5crleR5XX9bGpp5GDp0cw/aOMD6Z5Of7TmF7R7lxWxfrOss/9kYuHSshKNkJfC8MSAIPAl8M0vIGJcaYy4BtwPtykr4E3GuM6cg5p4hcIhYKOlK+z/T0+cunJ1MZRidmeOLpfnY9eQyALWubeN6OnoqVM1t3ax3drXXcmEgzMDxJT0c9Mc8jGj3//xKvji5JmebTUFvF86/p4TUvuZJvPXqEX9hTjJ1N8P1fHGd9VwObuhtoaaghXrO85ZSLz0oISrbjApBZ1toZY8zBIG2h4+D8oGUv4OEClkfLVchSJVMZDveNLcugsIU+mJdKNOrRODLN+PgU6bSrg9U0QG4p63i+est+2sfVecPgJBPj06TTGbKPOucMZbgMPj744GaK+mR898fnB+Xyfd89El77ufRzy+5+yWRc/ozvk874ZHyfTMYnk2H293QmA3iEVe95Hql0hmTK/aSDPBHPI+P7bg+bIC187fHJBIlkmplkmplEhulEiunEuYNMr9/aydWXtS7531G8OsrVl7exZb1rCVmq7plS1FRFuWpzGxu66nnq2SGe6R3h+KkJjgfdTh7Q0uj2+amLx6iORUilfVJpd53iNVF27x9kYjJJOhNcu7SPj08sGqEuHuPA8TOcHp4ikUqT8X0inkc04hGNRqitiXK4f4L+oQmSyUzwvEv3PPceqa2JMTI+Q//gJKl0BjxXLjy3pWK8JsrZ6RQnTp0llXbvgfCau/QYiVSG4wMTJNNzK+9mvy3q4jEyPpwcPEsylZ57jcDYZAzP8+gbCtID4du/9mwM8OaOz6P2bBSfc1/j3HR3jtzXCFXFItTHq6iric7+3a3rrKc+XrXAFV56KyEoaQXybWU5Aiy093Zr8Jh7bNieWfy+3bAmEvFoaytf8+PYZIItm0opyurS3hxf7iJccrrb6pa7CBelSMSjriZGJOKRLw6MeBCLRVjf3XROuufhdg72IBb1zku/0PHF5FlseiVe444XuaAymUrPBpSr6P6D5+64cJ4bFrqNBq413QumX7dt4XSA67ctnF7IOUKRiEdbU3GfzWEw1txce971DTaBXFPUCXOshKAE8t+zefM8f6FjvXmeL8SM55pL+0o4Nq/WRn0Zi6xGVbGFuyYWm76aXkNWn0j+GV9rgEVte70SgpIR5lo9srUwz3iSrOMIjh3IOS47vRgtF84iIiIilbASFk/bR87YEWNMDbCFhYOSMC23wewqXCvJM+UqoIiIiFTeSghKHgZuNca0Zz13J1ATpOVlrT2MCzxem5P0OuBJzbwRERG5uKyE7pvPA+8AvmmM+RBzi6fdn71GiTHmP4A3Wmuzy/wPuMXSDgLfBV4J3AbcvlSFFxERkfJY9pYSa+0o8GLc8vJfwwUkDwB/lpM1GvxkH/tV4E3Aq4Hv4AKS12o1VxERkYuPt5rWjBAREZGL17K3lIiIiIiAghIRERFZIRSUiIiIyIqgoERERERWBAUlIiIisiIoKBEREZEVQUGJiIiIrAgrYUVXWcWMMbXA3wOvB3qAPuBea+0HsvJUAR8E/hRoBn4G/KW19tdLXuBVxBhzA/AkMGWtbchJU52XgTEmCrwL+APcvlsxYA/wAWvt9/Pkfzfwdtzfwh7gPdbaHy5ZgVcJY8xW4NPALbiFNx8A7rHWTi1rwVYJY8wf4T6zbwDagIPAvwKft9ZmsvLtBD6M24PuOPBJa+3nFvPaaimRigk+sB8CXoXbEuA2XICSysn6KeAvgjyvDNK/b4zpWbrSri7GGA/4LHB6niyq8/KoBd4H/Aq3uvQfAyeA7xpjXpadMQhIPoK7LjuBZ4FvG2OuWdISX+SMMS3AI0Aj7rPl3bgv0C8sZ7lWmXcBM8B7gJcB38AFgR8LMxhjbga+CfwS+H3gPuAzxpi3LOaF1VIilfRm4Hpgm7V2IF8GY8w64M+Bu621XwieewI4DPwVcM8SlXW1eRPQAXwRuDs7QXVeVlPAZdbakfAJY8wuYCvug/2h4Lka4P3Av1hrPxE89yNca8nf4YIZKczbgFbg2nDjVWNMCrjfGPPh7D3TpGQvt9Zm39D8wBjTALzdGPN+a+0M7obml9baN2fl2Qh80BjzxewWlWKopUQq6c3AV+YLSAK34fY0+u/wCWvtOPC/uCZxKVJwJ/lR4J1AIk8W1XmZWGvT2QFJ8JyPazlZm/X083HdZA9kHwt8GdgZtGxJYXYC38vZCf5B3J39zuUp0uqSE5CEdgNxoC0Isl9M1mdI4H5gDXBdqa+toEQqwhhTjWslOWaM+S9jzKQxZswY8yVjTHtW1u3AgLV2OOcUe91pjN6jxfsn4BfW2ofmSVedV1BQf88Hsu/YtwePz+Rk34vrhli3BEVbLbZzbt0S3LkfZK6epfxuAYaBU8AWoJqc64B7P8MiroM+fKRS2nHdg38LtAB3AO8AXsK50XUrMJrn+BGgCmjIkybzMMZci2uheucC2VTnlfUOwOB2PA+1AjN5BmKGrSxtS1GwVWKh96/qsQKMMb+N6xL+VNDC1xok5V6HRb+fNaZECmaMacY1zV3IYeYC3lHgVdbaRHCOceBBY8xN1tongzz5tqr2Fki7ZBRZ5wncIMrPWWtz78hzqc7nUUydB3fo2ce+APg48Alr7Y9z8qvOy2e+ulQ9llkw+P1B3Ey+j+Ukz1ffJV8HBSVSjDuBewvIdx2wP/j9sTAgCTwSPF6Ne5OPMBd1Z2sBkrjpfpeyYup8G25a6uuDcSXg+oDDcSbT1tppVOcXUkyd/yr8hzHmt3CzEb6BayHMNgLEjTHx4BqEWrLSpTALvX81yLWMggD928Ak8AprbTJICt+vudehNSe9aApKpGDW2vtw074KYow5ukByODJ7H9BljGnLGeNwlXvJ0kZwrxbF1Lkx5g7ch8KRPMkjuLuce1CdL6jY9zmAMWYL8B3c9Mg3BINds4VflttxAwZDVwHjuGnEUph95IxZCAZebsHNNpMyMMbEgf8BuoGbrbVDWckHcS2z24H/y3r+quCx5OBQY0qkkh4Cfjf4wAi9JHh8KnjchQtQXhNmCKaevRz41lIUchW5D3hRzs9/AtPB7/8W5FOdl1HQvL0L6AfuyGkZDD0OnAFem3VcFHcNHs4TxMj8HgZuzRkwfydQE6TJIhljYsBXgOcAt1trz7nBDLotHyHrMyTwOtwCmbspkef7+luQyjDGbMIFH48Dn8FNkfwo8Ki19s6sfJ8F3oBb1+EobjGkG4Ad1tr+pS73amKM+Ufg3XlWdFWdl0GwYvFPcXfpdwHnTH+31j6RlTdcPO29uBaVtwB/CNxkrd2zVGW+2AVdkb/BtQh+COjCDSr+jrX2rmUs2qphjPk88Fbgb4Cf5CTvtdaOBYun/Rh3M3Q/8Du4VaLfZq3991JfW903UjHW2qPGmFtxHxhfw41VeBD3BZjtr4EJ3FTWcMnzW/XlWFGq8/Loxt1NghtLkit7/ZF/Dv59d3DcHmCnApLiWGtHjTEvxt3ofA033uEBzh/HI6V7afD48TxpLwJ+aK39qTHmlbhA+09wy8zfvZiABNRSIiIiIiuExpSIiIjIiqCgRERERFYEBSUiIiKyIigoERERkRVBQYmIiIisCApKREREZEVQUCIiIiIrgoISERERWREUlIiIiMiKoKBEREREVgQFJSIiIrIi/D+2cJgafEriAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "\n",
    "# mkdir('snr_weights')\n",
    "# SNR_vector = net.get_weight_SNR()\n",
    "# np.save('weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(SNR_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(10*np.log10(SNR_vector), norm_hist=False, label=name, ax=ax, hist_kws=dict(cumulative=True), kde_kws=dict(cumulative=False))\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Cumulative Density')\n",
    "ax.legend()\n",
    "plt.title('SNR (dB) CDF: Total parameters: %d' % (len(weight_vector)))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluate pruning performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]\n",
      "-inf\n",
      "params: 2395210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delete proportion: 0.000000\n",
      "\u001b[34m    Jtest = 0.108794, err = 0.026600\n",
      "\u001b[0m\n",
      "-20.818575 dB\n",
      "params: 2155688\n",
      "Delete proportion: 0.100000\n",
      "\u001b[34m    Jtest = 0.106250, err = 0.026100\n",
      "\u001b[0m\n",
      "-20.157747 dB\n",
      "params: 1916166\n",
      "Delete proportion: 0.200000\n",
      "\u001b[34m    Jtest = 0.105738, err = 0.026500\n",
      "\u001b[0m\n",
      "-18.925684 dB\n",
      "params: 1437126\n",
      "Delete proportion: 0.400000\n",
      "\u001b[34m    Jtest = 0.104454, err = 0.026600\n",
      "\u001b[0m\n",
      "-15.761734 dB\n",
      "params: 958084\n",
      "Delete proportion: 0.600000\n",
      "\u001b[34m    Jtest = 0.102157, err = 0.026600\n",
      "\u001b[0m\n",
      "-10.841445 dB\n",
      "params: 479042\n",
      "Delete proportion: 0.800000\n",
      "\u001b[34m    Jtest = 0.098468, err = 0.027600\n",
      "\u001b[0m\n",
      "-9.496842 dB\n",
      "params: 239521\n",
      "Delete proportion: 0.900000\n",
      "\u001b[34m    Jtest = 0.098064, err = 0.027300\n",
      "\u001b[0m\n",
      "-5.499096 dB\n",
      "params: 23953\n",
      "Delete proportion: 0.990000\n",
      "\u001b[34m    Jtest = 0.102343, err = 0.029800\n",
      "\u001b[0m\n",
      "-1.924860 dB\n",
      "params: 11977\n",
      "Delete proportion: 0.995000\n",
      "\u001b[34m    Jtest = 0.113258, err = 0.034000\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "delete_proportions = [0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]# 0.9999, 0.99999, 0.999999, 0.9999999, 0.99999999] # \n",
    "# keep_proportions = 1 - delete_proportions\n",
    "\n",
    "\n",
    "batch_size = 256\n",
    "Nsamples_predict = 100\n",
    "# Nsamples_KLD = 20\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "err_vec = np.zeros(len(delete_proportions))\n",
    "\n",
    "print(delete_proportions)\n",
    "\n",
    "for idx, p in enumerate(delete_proportions):\n",
    "    if p > 0:\n",
    "        min_snr = np.percentile(SNR_vector, p*100)\n",
    "        print('%f dB' % (10*np.log10(min_snr)))\n",
    "        og_state_dict, n_unmask = net.mask_model(Nsamples=0, thresh=min_snr)\n",
    "#         print(net.model.state_dict()) # for debug purposes\n",
    "        print('params: %d' % (n_unmask))\n",
    "    else:\n",
    "        print('-inf')\n",
    "        print('params: %d' % (net.get_nb_parameters()/2))\n",
    "    \n",
    "    test_cost = 0  # Note that these are per sample\n",
    "    test_err = 0\n",
    "    nb_samples = 0\n",
    "    for j, (x, y) in enumerate(valloader):\n",
    "        cost, err, probs = net.sample_eval(x, y, Nsamples_predict, logits=False) # , logits=True\n",
    "\n",
    "        test_cost += cost\n",
    "        test_err += err.cpu().numpy()\n",
    "        test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    test_cost /= nb_samples\n",
    "    test_err /= nb_samples\n",
    "\n",
    "    print('Delete proportion: %f' % (p))\n",
    "    cprint('b', '    Jtest = %1.6f, err = %1.6f\\n' % (test_cost, test_err))\n",
    "    err_vec[idx] = test_err\n",
    "    \n",
    "    if p > 0:\n",
    "        net.model.load_state_dict(og_state_dict)\n",
    "    \n",
    "    \n",
    "plt.figure(dpi=100)\n",
    "plt.plot(delete_proportions, err_vec, 'o-')\n",
    "\n",
    "np.save(results_dir+'/snr_proportions', delete_proportions)\n",
    "np.save(results_dir+'/snr_prune_err', err_vec)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.model.prior_instance = isotropic_gauss_prior(mu=0, sigma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### weight KLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aa = net.model.state_dict()\n",
    "# net.model.load_state_dict(aa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'KLD density: Total parameters: 2395210, Nsamples: 20')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "Nsamples = 20\n",
    "# mkdir('snr_weights')\n",
    "KLD_vector = net.get_weight_KLD(Nsamples)\n",
    "np.save(results_dir+'/kld_vector_'+name+'.npy', KLD_vector)\n",
    "\n",
    "print(KLD_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(KLD_vector, norm_hist=False, label=name, ax=ax)\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('KLD density: Total parameters: %d, Nsamples: %d' % (len(KLD_vector), Nsamples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'KLD CDF: Total parameters: 23928000, Nsamples: 20')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "# Nsamples = 20\n",
    "# mkdir('snr_weights')\n",
    "# KLD_vector = net.get_weight_KLD(Nsamples)\n",
    "# np.save('weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(KLD_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(KLD_vector, norm_hist=False, label=name, ax=ax, hist_kws=dict(cumulative=True))\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Cumulative Density')\n",
    "ax.legend()\n",
    "plt.title('KLD CDF: Total parameters: %d, Nsamples: %d' % (len(weight_vector), Nsamples))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluate pruning performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]\n",
      "-inf\n",
      "params: 2395210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delete proportion: 0.000000\n",
      "\u001b[34m    Jtest = 0.108462, err = 0.026500\n",
      "\u001b[0m\n",
      "-0.020359 nats\n",
      "params: 2156186\n",
      "Delete proportion: 0.100000\n",
      "\u001b[34m    Jtest = 0.105188, err = 0.026000\n",
      "\u001b[0m\n",
      "-0.003666 nats\n",
      "params: 1917323\n",
      "Delete proportion: 0.200000\n",
      "\u001b[34m    Jtest = 0.102933, err = 0.026200\n",
      "\u001b[0m\n",
      "0.020074 nats\n",
      "params: 1438665\n",
      "Delete proportion: 0.400000\n",
      "\u001b[34m    Jtest = 0.100827, err = 0.027000\n",
      "\u001b[0m\n",
      "0.067958 nats\n",
      "params: 958856\n",
      "Delete proportion: 0.600000\n",
      "\u001b[34m    Jtest = 0.099758, err = 0.028400\n",
      "\u001b[0m\n",
      "0.160577 nats\n",
      "params: 478426\n",
      "Delete proportion: 0.800000\n",
      "\u001b[34m    Jtest = 0.105860, err = 0.029900\n",
      "\u001b[0m\n",
      "0.235653 nats\n",
      "params: 238856\n",
      "Delete proportion: 0.900000\n",
      "\u001b[34m    Jtest = 0.110121, err = 0.031400\n",
      "\u001b[0m\n",
      "0.388317 nats\n",
      "params: 23850\n",
      "Delete proportion: 0.990000\n",
      "\u001b[34m    Jtest = 0.144586, err = 0.039400\n",
      "\u001b[0m\n",
      "0.428555 nats\n",
      "params: 11990\n",
      "Delete proportion: 0.995000\n",
      "\u001b[34m    Jtest = 0.151927, err = 0.040900\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "delete_proportions = [0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]# 0.9999, 0.99999, 0.999999, 0.9999999, 0.99999999] # \n",
    "# keep_proportions = 1 - delete_proportions\n",
    "\n",
    "\n",
    "batch_size = 256\n",
    "Nsamples_predict = 100\n",
    "# Nsamples_KLD = 20\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "err_vec = np.zeros(len(delete_proportions))\n",
    "\n",
    "print(delete_proportions)\n",
    "\n",
    "for idx, p in enumerate(delete_proportions):\n",
    "    if p > 0:\n",
    "        min_kld = np.percentile(KLD_vector, p*100)\n",
    "        print('%f nats' % (min_kld))\n",
    "        og_state_dict, n_unmask = net.mask_model(Nsamples=20, thresh=min_kld)\n",
    "#         print(net.model.state_dict()) # for debug purposes\n",
    "        print('params: %d' % (n_unmask))\n",
    "    else:\n",
    "        print('-inf')\n",
    "        print('params: %d' % (net.get_nb_parameters()/2))\n",
    "    \n",
    "    test_cost = 0  # Note that these are per sample\n",
    "    test_err = 0\n",
    "    nb_samples = 0\n",
    "    for j, (x, y) in enumerate(valloader):\n",
    "        cost, err, probs = net.sample_eval(x, y, Nsamples_predict, logits=False) # , logits=True\n",
    "\n",
    "        test_cost += cost\n",
    "        test_err += err.cpu().numpy()\n",
    "        test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    test_cost /= nb_samples\n",
    "    test_err /= nb_samples\n",
    "\n",
    "    print('Delete proportion: %f' % (p))\n",
    "    cprint('b', '    Jtest = %1.6f, err = %1.6f\\n' % (test_cost, test_err))\n",
    "    err_vec[idx] = test_err\n",
    "    \n",
    "    if p > 0:\n",
    "        net.model.load_state_dict(og_state_dict)\n",
    "    \n",
    "    \n",
    "plt.figure(dpi=100)\n",
    "plt.plot(delete_proportions, err_vec, 'o-')\n",
    "\n",
    "\n",
    "np.save(results_dir+'/kld_proportions', delete_proportions)\n",
    "np.save(results_dir+'/kld_prune_err', err_vec)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
